|=-----------------------------------------------------------------------=|
|=-----------=[    E0: Selective Symbolic Instrumentation    ]=----------=|
|=-----------=[ Powering Data-Flow Fuzzing and LLM Reasoning ]=----------=|
|=-----------------------------------------------------------------------=|
|=-----------------------------=[ Jex Amro ]=----------------------------=|
|=--------------------=[ @jexamro <[email protected]> ]=------------------=|
|=-----------------------------------------------------------------------=|

-- Table of contents

 0 - Introduction
 1 - Fuzzing
 2 - Symbolic Execution
 3 - Large Language Models (LLMs)
 4 - Towards a Hybrid Approach
     4.1 - Data-Flow vs Code-Coverage Guidance
     4.2 - E0 Design Decisions
 5 - E0 Architecture
 6 - Selective Symbolic Instrumentation
 7 - Symbolic Definitions
 8 - Data-Flow Coverage Metrics
 9 - Fine-Grained Memory Instrumentation via Hardware Watchpoints
10 - Validating E0: From Simple Benchmarks to Real-World Testing
11 - Case Study: CVE-2024-44297 in ImageIO
12 - LLM Integration & Symbolic-Guided Analysis
     12.1 - Instruction-Following Models
     12.2 - Guiding LLM Attention with Symbolic Expressions and Constraints
     12.3 - Code Context Representation
     12.4 - Context Expansion via MCP, A2A and On-Demand Decompilation
     12.5 - Modular Reasoning vs Chain-of-Thought Prompts
     12.6 - Reverse Prompt Engineering
     12.7 - Ensemble Consensus Prompting
     12.8 - Feedback Loop With Fuzzing and Symbolic Execution
13 - Alpha Source Code Release
14 - Acknowledgments
15 - Conclusion
16 - References
17 - Source Code

--[ 0 - Introduction

Imagine a fuzzer driven by data-flow rather than code-coverage. One that 
follows input def-use chains from source to sink, enforces constraints at 
each step, and reasons only about the precise, ordered low-level operations
that govern data propagation. Instead of blind randomness, it employs a 
mutation engine guided by symbolic reasoning and high-level semantic 
context.

Initially, LLMs remain in deep sleep, awakened only when their broader
insight is needed to analyze complex vulnerabilities requiring higher-
level semantic or cross-function reasoning. You can query this engine to 
inspect its findings, direct its focus, and steer exploration toward areas 
of interest.

We call this multi-layered binary analysis and instrumentation framework
E0 (Explore from Ground Zero). E0 integrates fuzzing's speed, selective 
symbolic instrumentation's precision, and LLM guidance's semantic 
understanding, leveraging each technique's strengths and minimizing 
their limitations.

While a full open-source release of the framework is planned, this 
Phrack issue includes an alpha snapshot of the most critical components 
supporting the techniques discussed here, enabling security researchers 
and engineers to directly experiment with and validate our novel methods. 
In this paper, we outline the challenges encountered in building E0 and 
introduce the solutions we developed.

--[ 1 - Fuzzing

It is mid-2025 and fuzzing remains the cornerstone of automated
vulnerability discovery, thanks to its ability to generate massive numbers
of diverse inputs at high speed, and to produce concrete, reproducible
failures (crashes, hangs, and memory violations) that demand developer
attention. By randomly mutating inputs guided by simple coverage
heuristics, modern fuzzers can traverse thousands of execution paths per
second with virtually no manual effort. However, fuzzing's randomized
nature and its over-reliance on code-coverage metrics often leave deep,
tightly guarded branches unexplored and miss subtle logic flaws or boundary
conditions. Furthermore, without any semantic understanding of program
state, fuzz campaigns can spend inordinate resources on redundant or
low-value paths, making it difficult to target high-impact code regions
without massive computational investment.

--[ 2 - Symbolic Execution

Symbolic execution fills fuzzing's blind spots by treating inputs as 
symbolic variables that are transformed into logical formulas and
systematically exploring program paths via constraint solving. Its strength
lies in generating concrete test cases for specific branches, uncovering
corner-case errors, assertion failures, and subtle boundary violations
without relying on coverage heuristics. For example, in a nested
buffer-copy loop it can derive an input that triggers an off-by-one
overflow or calculate the exact values needed to cause an integer 
overflow, or solve for a memory write that lands at a specific address.

Yet symbolic execution operates only at the level of individual 
instructions and their accumulated formulas; it has no built-in notion of 
buffer sizes, object lifetimes, or business-logic rules. Without manually 
supplied checks, such as boundary conditions, function summaries, loop 
invariants, or custom heap models, many critical bugs go undetected. These 
include out-of-bounds writes, use-after-free conditions, and missing 
validations across multi-step transactions. Detecting a use-after-free, 
for example, requires encoding both allocation and deallocation semantics 
by hand, since the solver cannot distinguish freed memory from valid 
regions.

This instruction-centric view also leads to explosive path growth: each
branch doubles the search space and quickly overwhelms time and memory 
resources on complex binaries. SMT solvers may timeout on large complex 
expressions, and external interactions such as network I/O, system calls, 
or threading often require hand-written stubs or rough approximations that 
can miss behaviors or introduce false negatives or positives. While 
strategies such as search heuristics (depth-first versus breadth-first), 
state merging, interpolation, constraint caching, and mixed concrete-
symbolic (Concolic) runs can alleviate some of the limitations, the core 
challenges of scalability, and broader contextual understanding remain.

--[ 3 - Large Language Models (LLMs)

By contrast, Large Language Models bring complementary strengths by
naturally absorbing and manipulating broader semantic layers (functions,
modules, design patterns), and synthesizing intent across thousands of
lines of code. For example, given a snippet like:

    .-----------------------------------.
    |  char buf[16];                    |
    |  write_user_data(buf, user_len);  |
    '-----------------------------------'

LLMs can warn: "Potential buffer overflow if 'user_len' > 16," without
requiring any additional boundary specifications.

In another snippet:

    .-----------------------------------.
    |  ptr = malloc(...);               |
    |  free(ptr);                       |
    |  ptr->field = x;                  |
    '-----------------------------------'

LLMs can flag a use-after-free issue: "You're writing to freed memory
here; ensure you don't access 'ptr' after it's freed." They can also spot
missing business invariants, such as failing to roll back a transaction on
failure, or suggest protocol fixes that span multiple functions.

Yet they still stumble over low-level assembly and raw binaries: they
hallucinate flag conditions ("this jump tests zero" when it actually tests
carry), cannot compute dynamic branch targets, and hit context-window
limits on very large repositories (truncating critical paths in a
20,000-line module). They struggle to pinpoint exact lines of code and
visualize deeply nested jump chains, especially when registers are reused
to hold different variables, which distracts their token-based attention.
Time and computational cost further constrain them: longer contexts incur
higher inference latency and significant compute expenses, making
exhaustive analysis of massive code bases impractical. Despite these
drawbacks, by leveraging prompt engineering, fine-tuning on domain-specific
code, Model Context Protocol (MCP), and retrieval-augmented generation,
researchers can still extract high-impact insights into memory-safety
flaws, race conditions, and business-logic errors.

However, LLMs frequently generate a high volume of false positives during
vulnerability analysis when they lack precise data-flow and constraint
information. In practice, an LLM may flag any external input as a potential
attack surface, even when the input cannot reach or influence the 
vulnerable code in question. This over-flagging forces researchers to 
manually review numerous false findings or to invoke additional LLM 
inference rounds, sometimes with narrower prompts or more compute-
intensive techniques, to validate true positives. While LLMs can deliver 
accurate results when a vulnerability is fully contained within a single 
function or module, real-world vulnerabilities often span multiple 
functions, modules, or processes. Reconstructing and supplying the full 
cross-function context is both challenging and expensive, in terms of 
engineering effort and inference latency, making validation and false-
positive reduction the primary hurdles in applying LLMs to large-scale 
binary vulnerability research.

--[ 4 - Towards a Hybrid Approach

The contrast between symbolic execution's instruction-by-instruction
precision and LLMs' broad, high-level reasoning highlights how they can
address each other's blind spots. Symbolic execution supplies exact
data-flow semantics: accumulated path constraints; variable liveness;
input reachability; solved constraint-variable ranges; and cross-thread
flows directly into AI pipelines, sparing LLMs the heavy lifting of
inferring low-level data-flow details across vast source-code or
decompiled binaries and thus dramatically reducing the volume of false
positives that undermine purely LLM-driven analyses. In addition, since
compiler optimizations and decompilation can alter or obscure actual
control-flow and binary behavior, relying solely on source or decompiled
code risks missing hidden bugs, whereas symbolic slicing operates on the
real binary, preserving true execution behavior.

In contrast, LLMs inject domain context, established design patterns, and
high-level business-logic checks that symbolic tools cannot model. 
Combined in a dynamic feedback loop (fuzzing, on-demand symbolic slicing, 
and LLM-guided review), you achieve a self-reinforcing pipeline: fuzzers 
explore new paths informed by solved constraints; symbolic analysis 
attaches precise data-flow invariants; and LLMs transform those invariants
into actionable, human-readable insights, all validated through native
binary execution.

An ideal hybrid system harnesses the raw throughput of unmodified binaries;
triggers symbolic emulation only on input-derived code regions for minimal
overhead; and enriches each LLM prompt with concise slices scoped to one
or more functions (including their constraint inputs: arguments,
memory-load values, and call-return values), augmented by SMT-solved
variable ranges to eliminate infeasible scenarios up front. This fusion
arms LLMs with concrete, high-fidelity context; reduces wasted inference
cycles on false positives; and closes the loop with end-to-end fuzzing,
uniting discovery breadth, symbolic precision, and AI reasoning into one
scalable, low-latency vulnerability-discovery engine.

---[ 4.1 - Data-Flow vs Code-Coverage Guidance

Data-Flow Guidance centers input exploration on the precise propagation of
attacker-controlled data through program operations. By instrumenting and
tracking def-use chains (capturing only those instructions that carry data
derived from guarded inputs), E0 ensures that mutations and analyses focus
exclusively on branches and operations aligned with data-flow. In contrast,
Code-Coverage Guidance simply chases new basic blocks or branch targets 
without regard to semantic relevance. It may exercise the same edges 
thousands or even millions of times, wasting fuzz cycles on paths that 
bear no relation to exploitable state. Its only advantage is raw 
throughput: high volumes of test cases with minimal semantic insight.

Data-Flow Guidance reduces noise, yields higher-quality test cases in 
fewer iterations, and in our approach, supplies precise data-flow context 
for downstream symbolic and AI layers to perform deeper reasoning, predict 
and help discover vulnerabilities, and generate inputs that trigger them. 
Moreover, unlike Code-Coverage Guidance (which requires its monitoring 
scope to be tied to specific modules or regions), data-flow 
instrumentation via (SSI) Selective Symbolic Instrumentation is applicable 
across all modules and threads (with optional scope narrowing when 
needed), enabling E0 to track untrusted-input data-flow throughout the 
entire application without sacrificing performance.

---[ 4.2 - E0 Design Decisions

After extensive research and development, and multiple full rewrites, E0's 
design has converged on several core principles:

- Harness-Based Fuzzer Compatibility: E0 operates exactly like a modern
  fuzzer by executing a user-supplied harness iteratively. Users provide 
  a harness for the target functionality, along with a module name, a
  relative virtual address, and a register to capture, mark, and guard
  inputs. On each iteration, E0 takes the current sample and spawns new
  samples for every newly solved path constraint. With the optional -x
  flag, E0 will recursively execute each newly generated sample to 
  further expand its exploration.

- Crash-and-Hang Detection (in typical fuzzing fashion): the classic 
  "definite outcome" mechanism, where E0 drives your harness and watches 
  for native crashes, hangs, or illegal-memory accesses (even catching 
  them early in emulation mode before they'd be swallowed by OS exception 
  handlers).

- Decoupled AI Analysis: To maximize selective symbolic instrumentation
  performance, E0 separates the heavy lifting of data-flow tracking from
  downstream AI processing. When run with the -log_symdefs flag, E0 writes 
  collected symbolic definitions into a SQLite database, which AI
  agents can consume asynchronously in a separate process.

- AI-Driven Prediction: symbolic data is fed to LLMs that flag likely
  vulnerabilities ahead of an actual trigger, and in the near future, will
  loop back to the fuzzer to validate generated candidate inputs.

- Selective Instrumentation: We combine hardware watchpoints with OS-level
  page guards, then fall back exclusively on recursive, bitmap-driven
  watchpoint allocation for minimal overhead and maximal precision.

- On-Demand Emulation: By using Triton's ONLY_ON_SYMBOLIZED mode, symbolic
  analysis is invoked only when a guarded memory access introduces a new
  symbolic operand, then immediately reverts to native execution once all
  symbolic state is concretized.

- Layered Integration: Fuzzing provides breadth, the SMT solver refines
  precise path constraints, and an LLM-driven AI layer supplies high-level
  semantic reasoning. This synergy balances speed, accuracy, and contextual
  insight.

These decisions enable the framework to operate from ground zero on 
closed-source binaries, scale across large, multithreaded codebases, and 
supply rich low-level symbolic insights to the AI layer, paving the way 
for high-confidence, low-overhead vulnerability discovery.

--[ 5 - E0 Architecture

E0 is architected as a multi-layered framework in which each component
contributes to an integrated vulnerability discovery process.

                             .-----------------------------------------.
                             |                                         |
 .-------------.      .------V-------.      .-----------------.        |
 |   Samples   | ---> |    Fuzzer    | ---> |     Debugger    | <---.  |
 '-------------'      '--------------'      '-----------------'     |  |
       |                                              |             |  |
 .-------------.      .--------------.      .---------V----------.  |  |
 |   Mutation  | <--- |  SMT Solver  | <--- | Symbolic Execution |  |  |
 '-------------'      '--------------'      '--------------------'  |  |
                                                      |             |  |
 .-------------.      .--------------.      .---------V----------.  |  |
 |    Binja    | <--> |Python Binding| <--- |Symbolic Definitions|  |  |
 '-------------'   .--'--------------'      '--------------------'  |  |
        |          |                                                |  |
 .------V------.   |  .--------------.      .--------------.        |  |
 |    SQLite   | <-'  |      AI      |      |    MCP/A2A   |--------'  |
 |      db     | <--> |    Agents    | <--> |    Servers   |-----------'
 '-------------'      '--------------'      '--------------'


[1] - Fuzzer 
---------------------------------------------------------------------------
- Role: Drives input generation and verifies vulnerabilities.
- Function: Iteratively produces new inputs guided by solutions from the 
  SMT solver, targeting unexplored or under-explored paths to increase 
  overall path exploration and the likelihood of triggering 
  vulnerabilities. In addition, the fuzzer plays a role in vulnerability 
  verification by reproducing triggering conditions and validating 
  observable failures such as crashes or hangs.

[2] - Dynamic Instrumentation (Debugger - LiteTracer) 
---------------------------------------------------------------------------
- Role: Manages the seamless switching between native execution and 
  symbolic emulation. 
- Function: Monitors runtime execution using hardware watchpoints and
  protected memory pages. It triggers Targeted Emulation when a memory
  access on a traced guarded memory location is detected, ensuring that
  symbolic analysis is focused on relevant code paths.

[3] - Targeted Emulation (Selective Symbolic Execution) 
---------------------------------------------------------------------------
- Role: Performs symbolic analysis only when necessary and manages the
  transition back to native execution.  
- Function: Utilizes techniques such as Triton's ONLY_ON_SYMBOLIZED mode to
  process only instructions involving symbolic operands. Importantly, the
  term "Selective Symbolic Execution" here encompasses not only the
  optimization provided by Triton's ONLY_ON_SYMBOLIZED mode but also the
  overall targeted emulation strategy initiated by the dynamic selective
  instrumentation layer (Section 6) when critical memory accesses are
  detected. In addition, this component is responsible for switching back
  to native execution once all symbolic registers are concretized, thereby
  minimizing emulation overhead while capturing detailed symbolic
  expressions that reflect critical program behavior.

[4] - SMT Solver (Z3 - Bitwuzla)  
---------------------------------------------------------------------------
- Role: Solves accumulated symbolic constraints.  
- Function: Computes precise variable ranges and refines symbolic
  expressions before passing them to the AI analysis layer, ensuring 
  that the symbolic data is both accurate and actionable.

[5] - Binary IR and Decompiler (Binary Ninja)  
---------------------------------------------------------------------------
- Role: Provides detailed structural code context.  
- Function: Disassembles and decompiles binaries to extract function 
  boundaries, signatures, assembly code, pseudocode, and intermediate 
  representations (such as LLIL, MLIL, HLIL and SSA forms). These insights 
  help contextualize the symbolic expressions for subsequent LLM analysis.

[6] - AI Layer (LLM Integration)  
---------------------------------------------------------------------------
- Role: Conducts vulnerability analysis.
- Function: Leverages the well-structured symbolic expressions/definitions
  generated in the Selective Symbolic Execution layer (Section 6) to
  perform function-level or data-flow slices vulnerability assessments.

[7] - Model Context Protocol (MCP) & Agent-to-Agent (A2A)
---------------------------------------------------------------------------
- Status: Early research and development. 
- Role: Context retrieval and action invocation.  
- Function: Provides AI agents with a unified interface to fetch analysis
  data (function code, solved constraints, symbolic definitions) to enrich
  LLM context on-demand, and to invoke E0 operations such as fuzzing,
  debugging, sample generation, and verification.   

--[ 6 - Selective Symbolic Instrumentation (SSI)

Selective Symbolic Instrumentation tracks external inputs by installing 
targeted memory monitors. These monitors trigger symbolic analysis only 
when external inputs directly influence execution, minimizing unnecessary 
emulation overhead. Each symbolic slice precisely captures data-flow and 
control-flow semantics, producing structured constraints for downstream 
SMT solving and AI analysis. This ensures deep, meaningful analysis 
precisely where needed, while preserving native execution speed elsewhere.

The process begins by placing an OS-level guard on the input buffer's page.
Any load from this guarded page generates a fault, pinpointing the first
instruction to consume external bytes and spawning a focused symbolic
emulation slice seeded with a concrete program state. As symbolic values
propagate into registers, heap objects, stack locals, or globals, each
newly touched page is likewise guarded. Subsequent faults cascade, mapping
out the data-flow graph one step at a time as accumulated symbolic 
expressions, and ensuring that symbolic reasoning engages only when and
where inputs actually affect control or state.

 .-------------------------------------------------------------------.     
 |   Example: Symbolic Emulation Session on a memmove Loop           |     
 |-------------------------------------------------------------------|     
 |   memmove 0x1804ab304: stp q0, q1, [x3]  <- Symbolized mem write  |     
 |   memmove 0x1804ab308: add x3, x3, #0x20   // Not Symbolized      |     
 |   memmove 0x1804ab30c: ldnp q0, q1, [x1] <- triggers emulation    |     
 |   memmove 0x1804ab310: add x1, x1, #0x20   // Not Symbolized      |     
 |   memmove 0x1804ab314: subs x2, x2, #0x20  // Not Symbolized      |     
 |   memmove 0x1804ab318: b.hi #0x1804ab304   // Not Symbolized      |     
 '-------------------------------------------------------------------'     

In this example, emulation is triggered at the first 'ldnp' instruction 
(0x1804ab30c) when the guarded source memory is accessed. Symbolic 
execution starts using Triton's ONLY_ON_SYMBOLIZED mode. Emulating that 
'ldnp' instruction results in q0 and q1 registers being symbolized. As the 
loop continues, subsequent 'add' and 'subs' instructions will be emulated
by Triton but remain unmarked for data-flow since they do not operate on 
symbolized operands. At 0x1804ab304 'stp q0, q1, [x3]' Triton will 
symbolize the destination memory because the source registers are 
symbolized. A Triton memory-access callback, set by E0, is then triggered:
E0 guards the destination page, creating a new symbolic memory region.
Emulation proceeds until all symbolic registers have been concretized 
(i.e., no further symbolic registers dependencies remain - only symbolic 
memory), at which point emulation ends and all guards are lifted to allow 
the debugger to natively step over the emulated code. Native execution then
resumes until the next guarded memory access triggers another symbolic
emulation session.

The memmove example above represents a symbolic emulation session contained
entirely within a single function and basic block, effectively capturing a
concise slice of a selective symbolic emulation session. While the memmove 
session is bounded within one function, other symbolic sessions may span 
multiple functions. For instance, consider a more complex, cross-function 
slice that begins in Mod1.Func_A, continues into Mod2.Func_B, and then 
performs nested operations in Mod2.Func_C before returning to Mod2.Func_B:

                                                            Triggers
        Symbolic MEM load | Mod1.Func_A ldr w8, [x1] <----- Emulation
.-------------------------------------------------------------------------.
| # [LogMemSymbolicDef] MEM Symdef id: f4a70000c1fe                       |
| ((((SymVar_46) << 8 | SymVar_47) << 8 | SymVar_48) << 8 | SymVar_49)    |
'-------------------------------------------------------------------------'
        Symbolic w9       | Mod1.Func_A rev w9, w8
        Symbolic x0       | Mod1.Func_A mov x0, x9
        Symbolic RET x0   | Mod1.Func_A ret
.-------------------------------------------------------------------------.
| # [LogRetSymbolicDef] RET Symdef id: c0ffee0012c4                       |
| ((((SymVar_49) << 8 | SymVar_48) << 8 | SymVar_47) << 8 | SymVar_46)    |
'-------------------------------------------------------------------------'
        Symbolic x22      | Mod2.Func_B mov x22, x0
        Not Symbolic      | Mod2.Func_B b #0x18c328000
        Symbolic Branch   | Mod2.Func_B cbz w22, #0x18c328120
        Symbolic x0       | Mod2.Func_B add x0, x22, #8
        Symbolic Call     | Mod2.Func_B bl #0x18c2b000
.-------------------------------------------------------------------------.
| # [LogArgSymbolicDef] ARG Symdef id: 1360c0000ec814                     |
| ((((((SymVar_49) << 8 | SymVar_48) << 8 | SymVar_47) << 8 | SymVar_46)  |
|  + 0x8) & 0xffffffff)                                                   |
'-------------------------------------------------------------------------'
        Symbolic MEM load | Mod2.Func_C ldp x8, x9, [x0, #0x10]
.-------------------------------------------------------------------------.
| # [LogMemSymbolicDef] MEM Symdef id: ba5eba11dc0c                       |
'-------------------------------------------------------------------------'
        Symbolic x10      | Mod2.Func_C add x10, x9, #3
        Symbolic cmp      | Mod2.Func_C cmp x10, x8
        Symbolic Branch   | Mod2.Func_C b.hs #0x18c2b100
        Not Symbolic      | Mod2.Func_C ldr x8, [x0, #8]
        Symbolic MEM load | Mod2.Func_C ldr w8, [x8, x9]
.-------------------------------------------------------------------------.
| # [LogMemSymbolicDef] MEM Symdef id: 3a6e00ddeca8                       |
'-------------------------------------------------------------------------'
        Not Symbolic      | Mod2.Func_C ldrb w10, [x0, #0x44]
        Symbolic w11      | Mod2.Func_C rev w11, w8
        Not Symbolic      | Mod2.Func_C cmp w10, #0
        Symbolic w8       | Mod2.Func_C csel w8, w8, w11, ne
        Symbolic x9       | Mod2.Func_C add x9, x9, #4
        Symbolic MEM Store| Mod2.Func_C str x9, [x0, #0x18]
        Symbolic x0       | Mod2.Func_C mov x0, x8
        Symbolic RET x0   | Mod2.Func_C ret
.-------------------------------------------------------------------------.
| # [LogRetSymbolicDef] RET Symdef id: 87adf00d13d4                       |
'-------------------------------------------------------------------------'
        Not Symbolic      | Mod2.Func_B ldr x8, [sp, #0x58]
        Not Symbolic      | Mod2.Func_B sub x23, x8, #8
        Symbolic w20      | Mod2.Func_B adds w20, w0, #8
        Symbolic Branch   | Mod2.Func_B b.hs #0x18c329000
        ... Emulation continues until no more symbolic registers

In this multi-function session, emulation begins when Mod1.Func_A executes 
the guarded load at 'ldr w8, [x1]', producing a symbolic definition 
(MEM Symdef id: f4a70000c1fe). Emulation continues through symbolic rev, 
mov, and ret instructions in Mod1.Func_A, then picks up in Mod2.Func_B 
with the incoming symbolic return value x0: producing a return-boundary 
definition (RET Symdef id: c0ffee0012c4). It branches conditionally, calls 
into Mod2.Func_C: producing an argument-boundary definition (ARG Symdef 
id: 1360c0000ec814) for the call, and then in Mod2.Func_C multiple memory 
loads and arithmetic operations extend the slice before returning back to 
Mod2.Func_B with a final symbolic return value (RET Symdef id: 
87adf00d13d4) for post-call handling.

Throughout this extended session, E0 logs symbolic definitions (Symdefs) 
at memory loads, function returns, and argument passages to capture the 
precise data-flow def-use chains across-function boundaries. Definitions 
are captured exactly at the function-boundary events "ARG, MEM, and RET" 
where their symbolic expression holds the full def-use data-flow 
information accumulated from all previously emulated and fused symbolic 
slices/sessions, tracing back to the root symbolic inputs.

The next Abstract syntax trees (ASTs) samples "collected via Triton - in
AST Python Representation format" treat each SymVar_n as a root symbolic 
input corresponding to the n-th byte of a guarded input buffer, where E0 
has already marked every byte of that buffer as symbolic. When a load 
reads four consecutive bytes from memory, Triton simply retrieves those 
preexisting SymVar_ nodes and concatenates them via nested shift/or 
operations to form the AST. For instance:

.-------------------------------------------------------------------------.
| > ldr w8, [x1]                                                          |
| > loads 4 bytes from memory, resulting in w8 AST:                       |
|   ((((SymVar_46) << 8 | SymVar_47) << 8 | SymVar_48) << 8 | SymVar_49)  |
|-------------------------------------------------------------------------|
| > rev w9, w8                                                            |
| > produces w9 AST:                                                      |
|   ((((SymVar_49) << 8 | SymVar_48) << 8 | SymVar_47) << 8 | SymVar_46)  |
|-------------------------------------------------------------------------|
| > add x0, x22, #8                                                       |
| > results in x0 AST:                                                    |
|   (((((SymVar_49) << 8 | SymVar_48) << 8 | SymVar_47) << 8 | SymVar_46) |
|    + 0x8) & 0xffffffff                                                  |
'-------------------------------------------------------------------------'

Emulation only ends once all live symbolic registers are concretized, 
at which point the system reverts to native execution with "only" guarded 
symbolic memory to catch the next guarded memory access and initiate the 
next symbolic emulation session. Where multiple symbolic emulation 
sessions separated by native execution gaps will all contribute in 
building the input data-flow def-use chains.

As another illustration, consider how a later session incorporates the 
full upstream def-use chain. During this run, 379 symbolic emulation 
sessions occurred (with native execution in between), and by session #371, 
a single guarded load reflects the accumulated expressions along its 
def-use chain from the previous 370 sessions. For example:

---------------------------------------------------------------------------
> ldr x8, [x0]
---------------------------------------------------------------------------
This instruction loads from a guarded address, producing an AST that 
concatenates bytes defined (and shifted) across multiple earlier 
operations in multiple earlier emulation sessions. The resulting AST is:
---------------------------------------------------------------------------
((((((((SymVar_30) << 8 | SymVar_29) << 8 | SymVar_28) << 8 | SymVar_27) 
<< 8 | SymVar_26) << 8 | SymVar_25) << 8 | SymVar_24) << 8 | 
  (((((SymVar_23 << (0x38 & 0x3f)) & 0xffffffffffffffff) |
      (((SymVar_22 << (0x30 & 0x3f)) & 0xffffffffffffffff) |
        (((SymVar_21 << (0x28 & 0x3f)) & 0xffffffffffffffff) |
          (((SymVar_20 << (0x20 & 0x3f)) & 0xffffffffffffffff) |
            (((SymVar_19 << (0x18 & 0x3f)) & 0xffffffffffffffff) |
              (((SymVar_18 << (0x10 & 0x3f)) & 0xffffffffffffffff) |
                (((SymVar_17 << (0x8 & 0x3f)) & 0xffffffffffffffff) |
                  SymVar_16))))))) >> 56) & 0xff))
---------------------------------------------------------------------------

This mechanism enables efficient, guided symbolic sessions fused 
seamlessly with native execution by using memory-access instrumentation as 
both the trigger and steering signal for symbolic analysis, bounding each 
slice to the scope of live symbolic dependencies. Unlike Concolic- 
execution, Selective Symbolic Instrumentation (SSI) avoids needless 
emulation of code unaffected by external inputs and preserves near-native 
performance across uninstrumented paths.

--[ 7 - Symbolic Definitions

E0 distinguishes between two complementary types of symbolic definitions:

(1) Root Symbolic Definitions anchor def-use chains at their origin, 
usually an input buffer. Users explicitly define these through command-
line parameters specifying the buffer location, size, and register details 
for tracing:

 -module     : Name of the module containing the input-handling code  
 -bp         : Breakpoint RVA to intercept and guard the input buffer  
 -reg        : Register holding the pointer to the input  
 -reg-offset : Optional offset if the input resides inside a structure  
 -i          : Start index of the guarded input region 
 -n          : Length of the guarded region in bytes  

For instance, the following command configures E0 to set a breakpoint at 
RVA '0x3b40' in the harness module 'ImageIO_test', guarding 200 bytes of 
input starting at byte 4 in the buffer pointed to by register 'x0':

  ./e0 -i 4 -n 200 -module ImageIO_test -bp 0x3b40 -reg 0

(2) Function-Layer Symbolic Definitions (SymDefs) are automatically 
captured at runtime as symbolic def-use chain values cross-function 
boundaries. These include:

 - ARG: symbolic function arguments
 - MEM: symbolic memory loads
 - RET: symbolic return values

These SymDefs serve two main purposes:
 - To support high-resolution data-flow coverage (see next section), and  
 - To focus LLM analysis on function-level variables derived from 
   untrusted inputs.

The instrumentation engine logs and stores Function-Layer definitions 
(SymDefs) in a local SQLite database, which enables downstream 
deduplication, AI inspection, and coverage tracking.

---------------------------------------------------------------------------
SymDef IDs and Invocation IDs
---------------------------------------------------------------------------
Each SymDef ID is a 64-bit identifier encoding the symbolic event's
location and type, allowing easy tracking and reference throughout E0's 
analysis workflow. These SymDef IDs appear throughout the runtime logs and 
database as ARG, MEM, and RET symbolic definitions. Their format varies 
slightly depending on the definition type.

---------------------------------------------------------------------------
Each SymDef ID includes:
---------------------------------------------------------------------------
- Instruction RVA (high SymDef ID 32 bits)
- Opcode fingerprint (MEM) or edge RVA 
  (RET: callee->caller, ARG: caller->callee)
- 8-bit argument mask (ARG only)

---------------------------------------------------------------------------
SymDef IDs are computed as follows:
---------------------------------------------------------------------------
// MEM (load) definitions
uint64_t symdef_mem =
    ((uint64_t)pc_rva << 32)
  | *reinterpret_cast<const uint32_t*>(instruction.getOpcode());

// RET (return) definitions
uint64_t symdef_ret =
    ((uint64_t)return_target_rva << 32)
  | ret_instruction_rva;

// ARG (call) definitions
uint64_t base    = ((uint64_t)called_function_rva << 32)
                 | call_site_rva;
uint64_t cleared = base & 0x00FFFFFFFFFFFFFFULL; // clear bits 56-63
uint64_t symdef_arg = ((uint64_t)sym_args_mask << 56) | cleared;

---------------------------------------------------------------------------
Invocation IDs then build on the SymDef ID by adding AST complexity:
---------------------------------------------------------------------------
uint64_t invocation = symdef /* _mem, _ret or _arg */
                    + Ast->getLevel();

E0 stores both each SymDef ID and its corresponding invocation IDs 
(SymDef ID + AST level) in its local SQLite database for de-duplication 
and coverage tracking. In the future, we may incorporate a module-name 
hash into the RVA to guard against any potential collisions, but to date 
this simple 64-bit packing scheme has produced no ID conflicts.

--[ 8 - Data-Flow Coverage Metrics

Building on the selective symbolic instrumentation framework described in 
the previous section, E0 replaces traditional, bitmap-based code-coverage 
metrics with a suite of high-resolution data-flow coverage signals. 
Conventional coverage relies on a bitmap, one bit per basic block or edge, 
to record whether a region has been exercised, but this low-resolution 
mechanism ignores semantic relevance. In practice, two inputs may mark the 
same basic blocks as covered, yet one may carry no untrusted input into a 
critical function argument while the other precisely steers untrusted 
bytes into a vulnerable buffer. Data-Flow Coverage Guidance, by contrast,  
captures exactly which instructions, and at which points in the def-use 
graph, are influenced by symbolic ("guarded") inputs. Whenever a guarded 
memory load produces a new symbolic operand, E0 invokes Triton to launch a 
targeted emulation slice and logs a set of "Symbolic Definitions" (ARG, 
MEM, RET) that record the precise ASTs showing how input bytes flow into 
registers or memory. 

Over the course of a single sample run, unlike code-coverage guidance 
which typically instruments only one or few modules, our data-flow 
approach fuses these definitions across all modules and threads to 
construct a coherent, per-instruction map of data-flow. By tracking 
symbolic dependencies at this level, E0 can distinguish execution paths 
that visit identical basic blocks but differ in how untrusted bytes 
influence computation. This finer granularity enables more efficient 
sample mutation, reduces blind fuzzing on semantically irrelevant code 
paths, and supplies downstream layers (SMT solver and LLM) with richer, 
contextually precise information.

This fine-grained, instruction-level data-flow mapping supports multiple 
quantitative metrics, ranging from higher-level, less precise signals to 
lower-level, more precise signals, that E0 collects to guide data-flow 
coverage:

---------------------------------------------------------------------------
 1 | Symbolic Emulation Session Count per Run
---------------------------------------------------------------------------
Each time a guarded memory load produces a new symbolic operand, E0 
invokes Triton to launch a targeted emulation slice. The total number of 
such emulation slices/sessions per sample indicates how many distinct 
data-dependent "frontiers" the input has exercised. A higher session count 
implies deeper penetration into the def-use graph, reflecting multiple 
layers of data propagation. By tracking this count over time, the system
deprioritizes further mutations of inputs that repeatedly trigger the same 
sessions and instead focuses on samples that spawn novel emulation slices.
---------------------------------------------------------------------------
 2 | Symbolic Definition Count per Run
---------------------------------------------------------------------------
Within each emulation session, E0 logs every new symbolic definition, 
whether it arises from a guarded memory load (MEM), a function-call 
argument boundary (ARG), or a return value (RET). Recording the total 
number of definitions per sample run provides insight into (a) the depth 
of def-use propagation (for example, whether untrusted bytes reached 
multiple function boundaries) and (b) the exact points where input-derived 
data influenced control flow or memory. By comparing this count against 
previous runs, E0 identifies samples that expose new combinations of 
data-flow events, even if they traverse identical basic blocks.
---------------------------------------------------------------------------
 3 | Emulated Instructions Count per Run
---------------------------------------------------------------------------
Not all instructions in a targeted emulation slice involve symbolic 
operands; nevertheless, E0 counts every instruction that Triton processes 
under ONLY_ON_SYMBOLIZED mode from the moment a guarded load triggers 
emulation until all live symbolic registers have been concretized. This 
instruction count measures the symbolic-emulation workload per sample and 
serves as a proxy for the slice's complexity. The more instructions 
executed under symbolic semantics, the broader the data-flow fan-out 
across registers and memory. By normalizing mutation priorities against 
this metric, E0 favors inputs that exercise larger slices of 
data-dependent code, increasing the chance of uncovering vulnerabilities 
requiring complex arithmetic or multi-step memory manipulations.
---------------------------------------------------------------------------
 4 | Hash of Unique Emulated Instruction Addresses per Run
---------------------------------------------------------------------------
To detect overlapping symbolic-emulation regions across samples, E0 
maintains a running, order-insensitive hash over the set of all unique 
instruction (RVA) relative virtual addresses emulated under symbolic mode 
across all sessions, modules, and threads. Each time Triton symbolically 
processes an instruction, E0 inserts its address into a global hash 
accumulator. At the end of the run, this accumulator represents the 
"symbolic footprint" of that sample. Comparing hash values between runs 
reveals whether two samples touched the same set of data-dependent 
instructions (regardless of execution order or frequency). Distinct hashes 
indicate that a new sample has uncovered previously unexplored symbolic 
paths; identical hashes suggest redundant coverage. This high-resolution 
fingerprint far surpasses basic-block bitmaps by accounting for 
instruction-level context, multi-module interactions, and multi-threaded 
execution.
---------------------------------------------------------------------------
 5 | Unique SymDef ID Count per Run
---------------------------------------------------------------------------
Each time E0 captures a symbolic definition (SymDef), it checks whether 
that SymDef ID has been seen in previous runs. If not, the SymDef is 
marked as unique for this sample. The total number of unique SymDef IDs 
captured in a run reflects the number of new function-boundary propagation 
points discovered during execution. A higher count indicates that symbolic 
input has reached new regions of code and crossed additional def-use 
boundaries, expanding the observed symbolic data-flow graph. This simple 
metric helps identify samples that extend input influence into unexplored 
functions or modules.
---------------------------------------------------------------------------

Because these metrics span from broad-level (session count) to fine-
grained (instruction-address hash), E0 combines them into a unified 
data-flow coverage score that prioritizes samples driving novel or deeper 
symbolic activity; those that spawn new emulation sessions, introduce 
additional symbolic definitions, exercise previously unseen emulated 
instructions, or previously unseen symdefs. By focusing on inputs that 
uncover fresh data-flow frontiers rather than merely toggling basic-block 
bits, E0 rapidly directs exploration toward code regions where untrusted 
input meaningfully influences computation.

This data-flow orientation yields key benefits: precise tracking of how 
untrusted bytes propagate; reduced redundancy through instruction-level 
hashing and definition counts; and comprehensive cross-module, cross-
thread coverage via a unified instruction-address footprint. In short, 
Data-Flow Coverage Guidance transforms low-level symbolic events into 
high-resolution signals, enabling E0 to probe untrusted data paths with 
surgical accuracy and accelerate the discovery of deep, semantically 
relevant vulnerabilities.

--[ 9 - Fine-Grained Memory Instrumentation via Hardware Watchpoints

Page-level guarding introduced severe performance penalties and didn't
scale. To address this, E0 falls back on the CPU's built-in hardware
watchpoints, which can monitor much smaller, power-of-two-aligned (address 
and size) regions within a page. Because each core only provides four 
debug registers, we employ a hierarchical bitmap and recursive splitting 
algorithm to efficiently carve a 16 KB page into at most four minimally 
sized watchpoint blocks, often using even fewer when possible.

 .------------------------------------------------------------------.      
 | Hardware Watchpoint Constraints                                  |      
 |                                                                  |      
 |  - Only four watchpoints per core                                |      
 |  - Each WP must cover a 2^N-byte region, aligned to its size     |      
 |  - Any untracked byte in that region still fires an exception    |      
 |  - Scattered inputs force oversized regions -> spurious traps.   |      
 '------------------------------------------------------------------'      

Tracking an arbitrary set of bytes inside a 16KB page requires carving that
page into smaller power-of-two blocks. To do this efficiently, and within
the constraint of only four hardware watchpoint slots, we use a
three-level bitmap hierarchy and a recursive splitting strategy:

[ Page (16KB) ]
      |
  .---v---.       Level 1: 32 segments (512B each)
  |  seg  |   rootBitmap -> 1...0...1...0... (1 bit per 512B segment)
  '-------'
      |
  .---v---.       Level 2: 64 qwords per segment (8B each)
  | qword |   pageSegments[n] -> 0x... (1 bit per 8B qword)
  '-------'
      |
  .---v---.       Level 3: byte mask per qword
  |  byte |   pageQwords[i] -> 0b10110010 (1 bit per byte in the qword)
  '-------'

1. Level 1 (root): A 32-bit root bitmap marks which 512B segments contain 
   any tracked byte.
2. Level 2 (segments): For each set bit, a 64-bit segment mask identifies
   which 8-byte "qwords" within the segment are active.
3. Level 3 (bytes): For each active qword, an 8-bit mask specifies which
   individual bytes are being tracked (byte-select mask).
    
Once you have that hierarchy, you compute the smallest 2^N block covering
all active bits:

             [ Full page: 0...31 ] 
                     |
            +--------+---------+
            |                  |
        [0...15]            [16...31]   
            |                  |
        used?->yes          used?->no  
            |                  |
      select [0...15]      back to root
            |
      repeat split on [0...15]

If the optimal block still contains gaps or would occupy more than one
hardware slot, we recursively split the range into two halves and test
each for activity. This continues until we either:

- Narrow to a single power-of-two block covering all active bytes, or  
- Fill all four slots with disjoint blocks that together cover the entire
  tracked region with minimal overhead.

---------------------------------------------------------------------------
Here are four concrete examples on a 16 KB page at base 0x16bd7c000
---------------------------------------------------------------------------
  Example 1: Aligned 8-Byte Range                              
  Tracked bytes: 0x16bd7c0a0-0x16bd7c0a7                       
  Offset range: 0xa0-0xa7, already 8 Bytes power-of-two block
  Byte-select mask: 0b11111111
  > single watchpoint: [0xa0-0xa7]    
---------------------------------------------------------------------------
  Example 2: Two-Byte Selection in an 8-Byte Block
  Tracked bytes: 0x16bd7c0a0-0x16bd7c0a1
  Aligned 8 Bytes block: 0xa0-0xa7
  Byte-select mask: 0b00000011
  > single watchpoint covering [0xa0-0xa7] with 2 byte-select
---------------------------------------------------------------------------
  Example 3: Unaligned 8-Byte Range
  Tracked bytes: 0x16bd7c00b-0x16bd7c012
  Offset span: 0x0b-0x12 (8 B) crosses two 8 B blocks
  > minimal cover: 32 B block [0x00-0x1f]
  > algorithm may choose:
    - one 32 B watchpoint [0x00-0x1f]
    - two 8 B watchpoints [0x08-0x0f] & [0x10-0x17]
    based on available free watchpoint slots
  Note: a single 16 B watchpoint [0x08-0x17] is invalid
  because the CPU enforces power-of-two size and address alignment
---------------------------------------------------------------------------
  Example 4: Multiple Disjoint Ranges
  Tracked bytes:
    - 0x16bd7c0a0-0x16bd7c0a7 (8 B)
    - 0x16bd7c200-0x16bd7c207 (8 B)
    - 0x16bd7c500-0x16bd7c51f (32 B)
  > Three Watchpoints assignments:
    [1] 8 B @ 0xa0-0xa7, byte-select mask=0b11111111
    [2] 8 B @ 0x200-0x207, byte-select mask=0b11111111
    [3] 32 B @ 0x500-0x51f
  Note: Gaps > power-of-two aligned sizes force separate blocks
---------------------------------------------------------------------------

Dynamic switching adds another layer of economy: on each page-fault or
access trap, record the recently accessed address, then recompute the four
watchpoints to favour that area (so the next few accesses fire the minimum
number of context switches). In practice this means you only ever reprogram
the debug registers when the exceptions "jumps" outside the current watched
blocks, keeping overhead near zero during tight loops over the same region.

.-----------.      on WP fault at address X 
|  CPU Core |----> recompute up to 4 WPs covering X +/- delta
'-----------'      reprogram DBGWCR0-3 with new ranges | resume execution

By weaving bitmaps, recursive splitting, and fault-driven reconfiguration,
you transform limited hardware watchpoints into a finely tuned tracking
engine that precisely covers only the memory bytes and addresses you care
about, while maximizing unwatched gaps to avoid irrelevant memory-access
exceptions, eliminating floods of excessive page faults.

--[ 10 - Validating E0: From Simple Benchmarks to Real-World Testing

During the early development of Selective Symbolic Instrumentation in E0, 
we first validated the framework on a suite of simple, intentionally 
vulnerable programs. In these tests, E0 reliably discovered low-level
flaws like "out-of-bounds accesses, integer overflows, null dereferences, 
and the like" by mutating input samples along symbolically traced paths.
However, it struggled to uncover vulnerabilities that require higher-level
semantic reasoning, revealing a gap that ultimately motivated our AI-driven
analysis layer (detailed in Section 12). This AI layer enhances E0's 
ability to detect complex vulnerabilities by reasoning over symbolic 
outputs and cross-function context, complementing the symbolic engine's 
precision.

To measure E0's true capabilities, we then turned to a real-world, large-
scale target: Apple's ImageIO library. With virtually no prior experience 
in image-format research, we chose ImageIO precisely because it has 
undergone extensive fuzzing and security review, so finding anything new 
would be a strong signal of E0's power. In a brief, multi-hour session, E0 
became unresponsive because, at that stage, it lacked the ability to 
handle process hangs in monitoring the test harness. Investigating that 
behavior uncovered a previously unknown bug in ImageIO's WebP parser, 
later filed as CVE-2024-44297.

In the next section, we present a detailed case study of CVE-2024-44297,
walking through how E0 detected, isolated, and ultimately helped us
reproduce this real-world vulnerability.

--[ 11 - Case Study: CVE-2024-44297 in ImageIO

Apple's advisory accurately summarizes the impact: Processing a maliciously
crafted message may lead to a denial-of-service. The root cause was in the
IIO_Reader_WebP::validateWebP routine, which took an untrusted value from
the image, loaded into register w0 and used it in an unchecked arithmetic
operation, triggering an integer overflow. In the snippet below, an 
immediate value is added to w0 without any prior validation:

  .------------------------------------------------------------------.     
  | add     w26, w0, #0x8          // w26 = chunkSizeWithPadding     |     
  | ldr     x8, [sp, #0x68]        //                                |     
  | sub     x27, x8, #0x8          //                                |     
  | ldr     x8, [x20, #0x8]        // x8 = dataInfo->bufferSize.     |     
  | cmp     x8, w26                // compare bufferSize against w26 |     
  | b.ls    0x18b020ee8            // branch if bufferSize <= w26    |     
  '------------------------------------------------------------------'     

When E0 symbolically followed that branch, its SMT solver produced a value
of w0 = 0xFFFFFFF8. Adding 8 makes w26 wrap to zero, so the check always
passes. That crafted input was generated in the ninth round of mutations,
after eight earlier samples had gradually solved header and chunk-length
constraints to push parsing toward the final buffer boundary.

After the integer overflow, the parser repeatedly processed the same chunk,
hanging the decoding thread in an infinite loop. In real-world tests,
sending the malformed WebP to Apple's Messages app caused a zero-click
denial-of-service: the app would lock up on a blank screen, remain
unresponsive through both app and device restarts, and rapidly drain the
battery while causing the device to overheat.

Although the IIO_Reader_WebP::validateWebP routine, its caller, and all
downstream functions include multiple boundary checks to prevent out-of-
bounds accesses, the integer overflow that wrapped w26 to zero was the 
only way to bypass every guard and disrupt service availability. Because 
that flaw was located on a path directly explored by E0's SMT-based 
symbolic engine, E0 generated the triggering sample without any extra 
bug-finding logic. In contrast, vulnerabilities that fall outside those 
straightforward data-flow paths, requiring higher-level or cross-path 
reasoning, would be missed by symbolic execution alone. Rather than 
burdening the symbolic layer with ad hoc detection rules, we delegate 
those more complex cases to the AI analysis component. As described next.

--[ 12 - LLM Integration & Symbolic-Guided Analysis

E0 integrates large language models (LLMs) not as speculative classifiers, 
but as structured reasoning agents guided by symbolic context. Its 
analysis pipeline constrains and informs the model using real data-flow 
derived from symbolic definitions and SMT-solved constraints, expanding 
context dynamically via the Model Context Protocol (MCP) only when 
necessary. The result is an AI-assisted workflow that is grounded, 
verifiable, and modular by design.

---[ 12.1 - Instruction-Following Models

While there is a clear difference in scale between open-source LLMs and 
large frontier models, we found that this gap narrows significantly when 
prompts and context are crafted to align with the model's understanding 
and reasoning patterns. Performance improves substantially when prompts 
are phrased in a way that matches the model's strengths, especially when 
refined through reverse prompt engineering techniques discussed in Section 
(12.6). In this setting, smaller open-source models that excel at 
instruction following often outperform larger general-purpose models, 
particularly in tasks like vulnerability analysis, provided they are 
guided by accurate context and well-scoped prompts.

Modern LLMs vary widely in their ability to follow explicit instructions. 
For vulnerability analysis, instruction adherence is critical: imprecise 
or overly general reasoning can result in hallucinations, false positives,
or overlooked vulnerabilities.

In our experience, smaller instruction-tuned models consistently 
outperform larger general-purpose ones for tasks like symbolic def-use 
analysis. As a result, E0 adopts a dual-model strategy:

  - Phi-4 is used for function-level vulnerability analysis. Its 
    instruction-following behavior, predictable output formatting, and 
    minimal hallucination make it ideal for interpreting well-scoped 
    symbolic expressions and constraints.

  - Qwen3 is employed for tool-assisted reasoning and cross-function 
    analysis. It supports structured tool-calling and larger context 
    windows, making it well-suited for issuing get_function_code 
    requests via the MCP and tracking data across multiple functions.

This division of responsibilities helps balance precision and scalability 
across different stages of E0's AI-driven analysis pipeline.

---[ 12.2 - Guiding LLM Attention with Symbolic Expressions and Constraints

Effective vulnerability analysis using LLMs hinges on precisely directing 
the model's attention to semantically meaningful elements within code. E0 
achieves this by anchoring each prompt to symbolic expressions derived 
from real data-flow paths. Extracted during guarded memory access and 
symbolic emulation, these expressions originate from untrusted inputs and 
are refined with SMT-solved value ranges. 

Rather than issuing long, monolithic prompts that dilute model focus, E0 
deploys a modular prompting strategy. Each prompt is scoped to a single 
symbolic definition or a tightly related set of expressions. Each includes:

  - One or more symbolic expressions (e.g., x0_18, *x1_2)
  - Their SMT-solved value ranges
  - Decompiled pseudocode or IR from the associated function(s)

Prompts focus on what to analyze while deliberately omitting unnecessary 
exclusions. Instead of relying on vague or negatively framed instructions, 
each prompt directs the model's attention to the data-flow and security 
implications of a specific set of symbolic expressions.

This strategy eliminates the need to specify vulnerability classes 
(e.g., use-after-free or integer overflow). The model evaluates how 
untrusted data propagates through control flow, memory operations, and 
return values, without presupposing any specific bug type.

To demonstrate how E0 guides LLM attention using scoped symbolic inputs, 
the following prompts show concrete examples of how symbolic expressions, 
value ranges, and function context are packaged into focused vulnerability 
analysis queries.

.-------------------------------------------------------------------------.
| Sample Prompt #1 | System + User Prompt (with few constraints)          |
|-------------------------------------------------------------------------|
| System Prompt:                                                          |
|-------------------------------------------------------------------------|
| You are an expert in vulnerability researching C++ code. The user will  |
| provide:                                                                |
|  1. A C++ function, which has been decompiled from ARM64 assembly.      |
|  2. A list of one or more user controllable expression(s) in the        |
|     function.                                                           |
| The user only controls the result of the provided controllable          |
| expression(s) and nothing else.                                         |
|                                                                         |
| Perform a security analysis of the usage of the output result of the    |
| user provided controllable expression(s). All other uncontrolled inputs |
| to the function are safe. Do not make assumptions about subsequent      |
| operations that are not provided and/or do not exist in the provided    |
| function. Bypassing error handling and controlling the function return  |
| value is not a vulnerability.                                           |
|                                                                         |
| At the end of your response say Vulnerable or Not Vulnerable.           |
|-------------------------------------------------------------------------|
| User Prompt:                                                            |
|-------------------------------------------------------------------------|
| Controllable Expression(s):                                             |
| - "get32Value()"   : result range is 0x0 to 0xffffffff                  |
| - "int32_t x0_18"  : result range is 0x0 to 0xffffffff                  |
| - "int32_t x0_26"  : result range is 0x0 to 0xffff                      |
| - "int32_t x0_27"  : result range is 0x0 to 0xff                        |
| - "uint32_t x8_12" : result range is 0x1 to 0x9                         |
| - "uint32_t x9_3"  : result range is 0x8 to 0x20                        |
| - "x0_24" : result range is 0x0 to 0xffffffff                           |
|                                                                         |
| Function:                                                               |
| int64_t ABCPlugin::init(IIODictionary* arg1)                            |
| {                                                                       |
|     <function code ...>                                                 |
| }                                                                       |
'-------------------------------------------------------------------------'

The first prompt uses a structured System-User format with moderate 
constraints, including explicit negative instructions (e.g., what not to 
consider a vulnerability). In contrast, the second prompt is minimal and 
purely positive, containing no exclusions or negations. It leaves the 
model free to reason within the provided symbolic context alone.

.-------------------------------------------------------------------------.
| Sample Prompt #2 | Minimal Positive Prompt (with no constraints)        |
|-------------------------------------------------------------------------|
| User Prompt:                                                            |
|-------------------------------------------------------------------------|
| Conduct a comprehensive security analysis of the following function,    |
| when the output of the following controllable expression is fully       |
| controlled by the attacker. The attacker only controls the result of    |
| the provided controllable expression and nothing else.                  |
| Structure your response.                                                |
| At the end of your response say Vulnerable or Not Vulnerable:           |
| VULNERABLE/NOT VULNERABLE                                               |
|                                                                         |
| Controllable Expression:                                                |
| - "*(uint64_t*)((char*)arg6 + *(uint64_t*)(*(uint64_t*)arg6 - 0x138)) + |
| 0x28" : result range is 0x0 to 0xffffffff                               |
|                                                                         |
| Function:                                                               |
| int64_t sub_18be031d0(int32_t arg1, int32_t arg2, void** arg3,          |
|                       uint32_t arg4, char* arg5, int64_t* arg6)         |
| {                                                                       |
|     <function code ...>                                                 |
| }                                                                       |
'-------------------------------------------------------------------------'

These examples demonstrate how E0 grounds LLM reasoning in symbolic 
inputs, ensuring that prompts reflect only untrusted-input influence and 
feasible execution paths, driving both interpretability and a low 
false-positive rate.

---[ 12.3 - Code Context Representation

Modern LLMs reason best when provided with higher-level code 
representations. While E0's symbolic expressions originate from low-level 
assembly, these expressions are often difficult for LLMs to interpret in 
isolation due to register reuse, control-flow indirection, and a lack of 
named variables.

To improve interpretability and guide model focus, E0 translates each 
low-level symbolic definition into its corresponding higher-level 
representation, including the full function code and IR where the symbolic 
expression is captured or defined. These include:

  - Assembly
  - LLIL, MLIL, HLIL
  - MLIL SSA and HLIL SSA forms
  - Decompiled C-like pseudocode

  .--------------------------------------------------.
  |  For example, consider a symbolic memory load    |
  |--------------------------------------------------|
  |  Assembly    : ldrb w8, [x1]                     |
  |  MLIL SSA    : x8#1 = zx.d([arg2#0].b)           |
  |  HLIL        : zx.d(*arg2)                       |
  |  HLIL SSA    : zx.d(*arg2#0)                     |
  |  Pseudocode  : (uint32_t)*(uint8_t*)arg2         |
  '--------------------------------------------------'

  | This single symbolic expression is represented across multiple 
  | abstraction levels. SSA forms disambiguate variable lifetimes and 
  | data-flow, while decompiled pseudocode offers a readable, semantically
  | rich view suited for LLM interpretation. Each representation offers a 
  | distinct view to help ground symbolic definitions in their surrounding 
  | code context.

Among these formats, decompiled pseudocode consistently yields the best 
results during vulnerability analysis, as most LLMs are heavily trained on 
C-like syntax. SSA-form IR representations, particularly MLIL SSA, also 
prove valuable due to their static single assignment property, which helps 
the model track symbolic variables without aliasing or ambiguity.

To gather this context, E0 includes a Python bridge that queries symbolic
definitions stored in its runtime database and augments them with 
representation data fetched from Binary Ninja. This includes function
signatures, code bodies, and intermediate representations.

Although Binary Ninja was chosen for its Intermediate Representation (IR) 
ecosystem and Python API integration, the retrieval pipeline is modular. 
It can be adapted to other disassemblers and decompilers depending on user 
preference or available tooling.

By coupling symbolic expressions with high-level function context, E0
ensures that LLMs operate on precise, structured inputs. This reduces
confusion, improves reasoning accuracy, and more importantly, bridges
the gap between dynamic symbolic execution and static code understanding.
Each symbolic definition serves as an anchor point, enabling the model to
expand its reasoning across entire functions and into adjacent functions
in the call graph, while staying rooted in the concrete data-flow captured
during (SSI) selective symbolic instrumentation.

---[ 12.4 - Context Expansion via MCP, A2A and On-Demand Decompilation

While E0 prompts are initially scoped to precise symbolic expressions 
within individual functions, complex vulnerability analysis sometimes 
requires a broader view. In such cases, the LLM may issue tool-calling 
requests to retrieve additional context using the Model Context Protocol 
(MCP).

One example is the get_function_code tool call, where the LLM explicitly 
requests the complete code of one or more functions it deems relevant to 
the current analysis. The MCP server first checks the internal cache and 
the symbolic definition database; if the data is missing, it triggers a 
fallback to Binary Ninja backend to perform on-demand disassembly, IR 
recovery, and decompilation of the requested function(s).

This design keeps prompts minimal and focused, while still granting the 
LLM access to full static context when needed. However, repeated MCP 
requests can consume significant context window space and increase 
inference latency, especially when retrieving large functions or 
traversing cross-function logic.

To address this, E0 is evolving toward a more efficient Agent-to-Agent 
(A2A) architecture. In this model, the root LLM agent offloads specific 
questions (such as "What does this callee return?" or "Does this check 
ever fail?") to secondary agents with smaller, focused contexts. These 
sub-agents operate independently to retrieve only the necessary facts, 
summaries, or security inferences, which are then aggregated and returned 
to the root agent for final vulnerability classification.

Below is a typical A2A JSON request issued by the root agent to assess a 
callee function's behavior.

.-------------------------------------------------------------------------.
| Sample A2A Query                                                        |
|-------------------------------------------------------------------------|
| {                                                                       |
|   "sub_18c27aeb0": [                                                    |
|      "What is the purpose of 'sub_18c27aeb0' and does it return a       |
|      value that is bounded or validated before being used in memory     |
|      operations?",                                                      |
|      "Does this function handle error conditions and return a safe      |
|      default or zero when the input is invalid?"                        |
|   ]                                                                     |
| }                                                                       |
'-------------------------------------------------------------------------'

This strategy allows E0 to expand context dynamically without overwhelming 
the LLM or wasting token budget on irrelevant code. It also improves 
modularity and parallelism, enabling scalable analysis through a set of 
lightweight, focused AI agents, each focused on a narrow, verifiable 
reasoning task.

---[ 12.5 - Modular Reasoning vs Chain-of-Thought Prompts

Although Chain-of-Thought (CoT) prompting is often promoted for complex
reasoning tasks, we found that it introduces significant false positives
and negatives in the context of vulnerability analysis. When multiple
reasoning steps are embedded into a single prompt, LLMs often produce
contradictions, overlook symbolic constraints, or fixate on irrelevant
code paths, resulting in less reliable analysis.

E0 avoids this by decomposing the reasoning process into modular, 
independently controlled steps. Each step is executed within a separate 
inference context, scoped to a narrow analysis goal. These include 
evaluating a specific symbolic expression, analyzing a function-level 
security pattern, or interpreting a solved constraint range. The results 
are then aggregated externally, allowing the system to review, retry, or 
refine intermediate steps as needed.

This modular approach improves accuracy, interpretability, and debugging.
It also allows the orchestration layer to selectively retry or expand any
single reasoning step without discarding the entire analysis. Rather than
embedding multi-stage logic into a single inference window, E0 treats each
step as a query-and-response unit, giving the orchestration layer full
control over the analysis flow.

While future reasoning models may support more reliable structured CoT 
prompting and larger context windows, external modular reasoning remains 
more robust and transparent in our current pipeline. 

---[ 12.6 - Reverse Prompt Engineering

Reverse Prompt Engineering is a technique where the desired output is 
provided to the model, and the model is asked to generate the most 
accurate prompt that would produce that output. In our workflow, this 
method was adapted to address cases of false positives and negatives. 
We applied the technique per model to improve prompt clarity and 
effectiveness.

A key mechanism is instructing the model to critique its own prior output 
by asking questions like:

    | Which word or phrase in the original request 
    | led you to that incorrect assumption?

    | Which word or phrase in the original request would
    | you modify or remove to prevent this mistake?

In one real case, the symbolic expression *(uint64_t*)x10_1 was used in 
the function sub_221204180. When the prompt instructed the model to 
analyze its "value range", the model reported:

    - Out-of-Bounds Memory Access
    - Use-after-Free
    - Arbitrary Memory Access

However, the value loaded from the dereferenced pointer was not used 
in any memory access. Instead, it was treated as a plain operand in 
arithmetic operations. The pointer itself was not attacker-controlled. 
Only the value it pointed to was symbolized. When the prompt was revised 
to ask about the "output range" rather than the "value range," the model 
correctly responded:

    | No direct vulnerabilities observed; expression 
    | is only used in a bitwise operation and shift.

This minor vocabulary change eliminated the false positive and produced a 
more accurate analysis. Reverse Prompt Engineering revealed that the term 
"value" had implicitly led the model to focus on pointer dereferencing 
safety rather than the meaning of the loaded result.

This insight directly informed the vocabulary and framing of prompts used 
across E0's ensemble prompting system.

---[ 12.7 - Ensemble Consensus Prompting

Ensemble Consensus Prompting is a technique in which multiple 
independently phrased prompts are issued to analyze the same symbolic 
slice or untrusted input. Instead of embedding many rules or constraints 
into a single complex prompt, E0 distributes them across a sequence of 
prompt variants. Each prompt introduces a focused subset of constraints 
or directives, which improves model focus and reduces confusion.

A candidate vulnerability is elevated only when all prompt variants reach 
the same conclusion, confirming the presence of a vulnerability. In 
addition to strict consensus, E0 also tracks results of positive versus 
negative findings across prompts to assist with manual debugging and 
deeper analysis.

The following example illustrates how this process works:

- 1st Prompt @ PHI-4  :  25/110 Vulnerable | 85/110 Not Vulnerable |  23%
- 2nd Prompt @ PHI-4  :  13/25  Vulnerable | 12/25  Not Vulnerable |  52%
- 3rd Prompt @ PHI-4  :   8/13  Vulnerable |  5/13  Not Vulnerable |  61%
- 4th Prompt @ QWEN3  :   3/8   Vulnerable |  5/8   Not Vulnerable |  37%
- 5th Prompt @ QWEN3  :   2/3   Vulnerable |  1/3   Not Vulnerable |  67%
- 6th Prompt @ QWEN3  :   2/2   Vulnerable |  0/2   Not Vulnerable | 100%

| The 110 functions analyzed here are those intersected by symbolic 
| data-flow at function boundaries (ARG, MEM, or RET SymDefs), based on 
| thousands of dynamically instrumented samples that led to the discovery 
| of CVE-2024-44297 (detailed in Section 11), rediscovered here using 
| LLMs. While E0 encounters many more functions across modules and 
| binaries, only those traversed by actual symbolic input propagation 
| were selected for LLM evaluation.

The two discovered vulnerabilities (2/2) shown in the last step are real. 
One rediscovers CVE-2024-44297, as confirmed by the LLM responses shown in 
the following summaries, while the second is valid but appears to be an 
older vulnerability already mitigated in upstream patches. It involved 
validating a sample-size variable that fell outside the scope of the 
selective symbolic instrumentation trace. This missing upstream context 
inspired the Model Context Protocol and A2A context expansion strategies 
discussed in Section 12.4.

.-------------------------------------------------------------------------.
| PHI-4 [14B/Q8] Vulnerability Rediscovery                                |
|-------------------------------------------------------------------------|
| Denial of Service: By forcing the function into infinite loops or       |
| causing crashes through invalid memory accesses, an attacker could      |
| disrupt service.                                                        |
|-------------------------------------------------------------------------|
| Qwen3 [32B/Q8] Vulnerability Rediscovery                                |
|-------------------------------------------------------------------------|
| This vulnerability could be used as part of a denial-of-service attack, |
| causing the WebP parser to enter an infinite loop when processing a     |
| maliciously crafted file.                                               |
'-------------------------------------------------------------------------'

Ensemble consensus prompting incrementally applies constraints and filters 
results based on full agreement across multiple prompts. These prompts are 
intentionally selected to span a range of false positive tendencies, from 
medium to high, so that while each may be noisy on its own, their 
intersection amplifies signal over noise. Prompts are sequenced from 
higher to lower expected false positive rates, while eliminating the use 
of prompts prone to false negatives or over-constraint. This avoids 
missing critical issues while refining away inaccurate findings.

Each individual consensus prompt shown above results in a medium to high 
detection rate ranging from 23% to 100%, while their combined detection 
rate is less than 2 percent (2 out of 110).

The consensus model significantly reduces false positives by filtering out 
speculative or marginal findings that might arise under weaker LLM 
instruction settings. It ensures that only the most robust, symbolically 
grounded vulnerabilities, resilient to model variation, phrasing shifts, 
and reasoning depth, are surfaced and tightly aligned with the captured 
def-use chains.

---[ 12.8 - Feedback Loop With Fuzzing and Symbolic Execution

This component of E0 is experimental and still under active development.
When all prompt variants in an ensemble consensus reach the same conclusion
identifying a vulnerability, E0 issues a follow-up prompt requesting
concrete values for the controllable symbolic expressions (SymDefs) needed
to trigger the vulnerability.

These values, together with their corresponding SymDefs and the sample in
which the expressions were recorded, are fed back into E0's fuzzing and
instrumentation layers. The symbolic execution engine then instruments the
program to reach the same symbolic execution point, accumulates the path
constraints that lead to that expression, and solves for the input that
would produce the requested value.

A new sample is then generated that satisfies these constraints and is
executed in a controlled environment. If the predicted vulnerability is
triggered (e.g., by causing a crash, hang, or invalid memory access) it is
logged as a real vulnerability. If the condition is not triggered, the 
result is returned back to the AI layer for further refinement.

This feedback loop closes the gap between static LLM vulnerability 
analysis insight and dynamic vulnerability validation. It allows E0 to 
convert symbolically grounded LLM predictions into executable test cases 
and determine which LLM-identified risks manifest as real-world 
vulnerabilities.

E0's LLM analysis pipeline is a symbolically grounded, prompt-guided, 
context-expanding, and modular layer that precisely interprets untrusted 
inputs. It is driven entirely by programmatically generated prompts 
(SymDefs, SMT-solved ranges, and decompiler IR representations) rather 
than by open-ended reasoning. Each analysis step is explicitly scoped and 
isolated using structured phrasing, reverse-prompt engineering, and 
ensemble-consensus prompting. By fusing symbolic execution, SMT solving, 
decompiler IRs, targeted prompts, and external orchestration, E0 delivers 
an LLM integration stack that offers higher accuracy and actionable 
insights: bridging symbolic analysis with higher-level semantic reasoning.

--[ 13 - Alpha Source Code Release

This paper is accompanied by a partial source release of E0, a curated 
alpha snapshot of its core instrumentation and symbolic analysis modules. 
While not a complete codebase, the included components directly support 
the techniques and workflows discussed throughout this paper, including 
symbolic tracing, guarded memory access, hardware watchpoints, and 
constraint solving.

Included in this release:

- LiteTracer
   |  A selective symbolic tracer that integrates Triton with an
   |  extended TinyInst debugger. It instruments only the operations 
   |  that matter, on-demand, using hardware watchpoints to trap memory 
   |  access and trigger symbolic emulation.

- MemoryGuard
   |  A runtime protection module that configures page permissions 
   |  and manages AArch64 hardware watchpoints to monitor access to 
   |  untrusted input with efficient, fine-grained precision.

- PageMap
   |  A compact bit-manipulation structure for tracking guarded memory. 
   |  Enables clustering and reuse of limited hardware watchpoints 
   |  (only 4 CPU watchpoint slots available per core).

- E0_Sample_Runner_Functions
   |  Constraint-driven sample generation logic. Captures SMT constraints 
   |  during tracing and solves for new inputs that drive unexplored paths.

- Patched TinyInst Debugger (macOS/debugger.cpp)
   |  A modified version of TinyInst's macOS debugger module that adds 
   |  support for ARM64 hardware breakpoints and watchpoints. Also extends 
   |  exception handling to catch and recover from guarded memory access.

This alpha release targets ARM64 macOS systems only, and is provided as 
part of this Phrack issue. A full open-source release of the entire E0 
framework, including its symbolic orchestration layer, Python bindings, 
event pipeline, and LLM integration, is planned at a later date.

All code is released under the Apache License, Version 2.0.

--[ 14 - Acknowledgments

E0 builds upon the efforts of several outstanding open-source projects 
whose design and implementation significantly shaped this work.

Special thanks to:

  - Ivan Fratric and the TinyInst / Jackalope team  
    E0 reuses core components from Jackalope's sample delivery and 
    mutation logic, and extends TinyInst's macOS debugger to support 
    hardware breakpoints, watchpoints, and memory access instrumentation.

  - Jonathan Salwan and the Triton team  
    Triton serves as the symbolic execution engine at the core of E0's 
    tracing logic. Its efficient architecture and symbolic precision made 
    it the ideal backbone for runtime analysis.

We also wish to thank Richard Johnson for his invaluable guidance, 
detailed feedback, and critical insights during the preparation of this 
paper. His thoughtful reviews helped sharpen the structure and clarity of 
our arguments.

After exploring several architectural paths and full rewrites, we 
ultimately chose to avoid redundancy and instead build on top of these 
solid, actively maintained projects. This included following Jackalope's 
structural patterns, extending Triton's instruction semantics to support 
ARM64e, and handling Apple-specific instructions like pointer 
authentication on macOS.

We plan to contribute patches and improvements back to these upstream 
projects where applicable, and encourage others to build on this 
integration model when combining fuzzing, tracing, and symbolic execution 
in real-world workflows.

--[ 15 - Conclusion

We introduced a novel methodology combining selective symbolic
instrumentation, data-flow-guided fuzzing, and structured LLM-driven
analysis, along with its implementation in E0. By precisely tracking
external inputs and instrumenting only essential symbolic operations,
E0 prioritizes native execution and engages symbolic emulation only where 
needed, ensuring high performance without sacrificing symbolic depth.

Structured symbolic definitions (ARG, MEM, RET) serve as focused inputs 
for targeted LLM analysis, augmented by reverse prompt engineering, 
ensemble consensus prompting, and dynamic context retrieval. This modular 
approach decomposes complex analysis tasks into clearly defined, 
verifiable steps, significantly enhancing vulnerability detection accuracy 
and interpretability.

Future work will refine AI-driven feedback loops, optimize symbolic 
instrumentation triggers, and expand E0's evaluation across diverse 
closed-source targets. Ultimately, E0 provides not just a technical 
improvement but a practical, scalable framework for advancing binary 
vulnerability research.

--[ 16 - References

[1]  Jonathan Salwan (2015). Triton: Dynamic Binary Analysis Framework. 
     https://triton-library.github.io
[2]  Ivan Fratric (2020). Jackalope Fuzzer.
     https://github.com/jackalope-fuzzer/jackalope
[3]  Jonathan Salwan (2022) - ttexplore.
     https://github.com/JonathanSalwan/ttexplore
[4]  Ivan Fratric (2020). TinyInst: A Lightweight Dynamic Instrumentation 
     Framework. https://github.com/TinyInst/TinyInst
[5]  De Moura, L. & Bjorner, N. (2008). Z3: An Efficient SMT Solver.
[6]  Z3 Theorem Prover - https://github.com/Z3Prover/z3
[7]  Stephens, N., Grosen, J., Salls, C., Dutcher, A., Wang, R., Corbetta,
     J., Shoshitaishvili, Y., Kruegel, C., & Vigna, G. (2016). Driller:
     Augmenting fuzzing through selective symbolic execution. In 23rd 
     Annual Network and Distributed System Security Symposium (NDSS).
[8]  Binary Ninja - https://binary.ninja
[9]  Andriesse, D. (2017). Practical Binary Analysis - No Starch Press.
[10] Brian S. Pak (2012). Hybrid fuzz testing: Discovering software bugs 
     via fuzzing and symbolic execution (Master's thesis). 
     School of Computer Science, Carnegie Mellon University.
[11] Microsoft. (2024). Phi-4: A Large-Scale Language Model.
[12] Alibaba Cloud. (2025). Qwen3: Large Language Models.
     https://arxiv.org/abs/2505.09388
[13] ARM Ltd. (2013). ARM Architecture Reference Manual: ARMv8-A edition.
     https://developer.arm.com/documentation/ddi0487/latest
[14] Samuel GroB (2020). Fuzzing ImageIO - Project Zero.
     https://googleprojectzero.blogspot.com/2020/04/fuzzing-imageio.html
[15] Alfredo Ortega, (2024). AI-Powered Bug Hunting: From Neural Networks  
     to Practical Exploits. https://github.com/Neuroengine-vulns/
     autokaker/blob/main/doc/AI-powered-bughunting-aortega-paper.pdf
[16] Josh Watson (2018). Vulnerability Modeling with Binary Ninja 
     - Trail of Bits Blog. https://blog.trailofbits.com/2018/04/04/
     vulnerability-modeling-with-binary-ninja/
[17] Reno Robert (2025) - Mindshare: Using Binary Ninja API to Detect 
     Potential Use-After-Free Vulnerabilities - Zero Day Initiative Blog.
     https://www.zerodayinitiative.com/blog/2025/3/20/
[18] Sean Heelan (2025) - How I Used O3 to Find CVE-2025-37899: A Remote 
     Zero-Day Vulnerability in the Linux Kernel's SMB Implementation.
     https://sean.heelan.io/2025/05/22/
[19] Michal Zalewski (2014) - American Fuzzy Lop (AFL).
     https://lcamtuf.coredump.cx/afl/

--[ 17 - Source Code

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`
end

|=[ EOF ]=---------------------------------------------------------------=|