Beyond Model Capabilities: Why Codex Outperforms Claude in Security Audit Skills

Introduction: The AI Security Audit Landscape

In the rapidly evolving landscape of cybersecurity and code auditing, artificial intelligence has become an indispensable tool for security researchers and companies worldwide. The integration of AI into vulnerability detection and code review processes represents a paradigm shift in how we approach software security. However, not all AI models perform equally when tasked with the critical responsibility of identifying security flaws.

This comprehensive analysis explores a fascinating discovery: when applying the same audit methodology and skill framework, Codex (powered by GPT-5.4 xhigh) consistently demonstrates superior vulnerability detection capabilities compared to Claude Code (powered by Claude Opus 4.6 with 1M context window). This performance gap isn't merely about raw model intelligence—it stems from fundamental differences in architecture, training methodology, and cognitive approach to problem-solving.

Understanding the Core Challenge: Context Window Limitations

One of the most significant challenges in AI-assisted code auditing is the inherent limitation of context windows. When analyzing large codebases, even models with expansive context capacity can experience hallucinations or information loss. To address this, the audit skill framework employs a strategic approach: first analyzing the project's business flow, then selectively retrieving code from functions called within that flow.

This methodology, while effective in managing context constraints, reveals interesting behavioral differences between the two models. The subsequent sections delve deep into these differences and their implications for security audit effectiveness.

Architectural Foundations: How Training Shapes Behavior

Codex: The Iterative Verification Engine

Codex represents a fundamentally different approach to AI agent design. Built upon the o3 series models and refined through reinforcement learning, Codex operates on a simple yet powerful principle: iterate until the task passes validation. According to OpenAI's documentation on "Unrolling the Codex Agent Loop," the core workflow follows this pattern:

Analyze Intent → Call Tools → Get Results → Feedback to Context → Reason Again → Repeat Until Complete

This training methodology instills a critical characteristic in Codex: it treats every analysis step as a test case requiring explicit pass/fail validation. When an audit methodology instructs Codex to "enumerate each hypothesis," the model's reinforcement learning drives it to literally enumerate and verify each one sequentially. The reason is straightforward—skipping a step that leads to an incorrect final result would yield negative reward during training.

This creates what we might call "execution discipline"—a systematic, almost mechanical adherence to prescribed procedures. In security auditing, where missing a single verification step can mean the difference between catching a critical vulnerability and overlooking it entirely, this discipline proves invaluable.

Claude: The Global Understanding Specialist

Claude Opus 4.6, by contrast, excels at what we might term "structured, multi-step reasoning." Its strengths lie in comprehending system architecture, tracking dependency chains, and maintaining coherent reasoning across multiple files. The extended thinking mechanism enables Claude to maintain continuity throughout long reasoning chains, making it exceptional at tasks requiring holistic understanding.

However, this strength carries an inherent trade-off. Claude's cognitive approach prioritizes building coherent mental models of how systems work, rather than exhaustively exploring how systems might be broken. When analyzing a smart contract, for instance, Claude constructs a unified understanding: "This protocol implements balance checks on all deposits and has fee-on-transfer protection." Based on this model, it makes inferences about security.

The critical weakness emerges when the mental model is incomplete. If Claude's model correctly identifies that "the protocol has balance check protection," all paths involving deposits are inferred as secure—even if a specific path lacks this protection but wasn't included in the initial model. The mental model, being a compression of reality, inevitably loses details.

Comparative Analysis: Real-World Implications

Case Study: Fund Flow Analysis in ammHandleTransfer

A concrete example illustrates these differences starkly. In analyzing the ammHandleTransfer function's fund flow:

Claude's Approach:

• Constructed a global mental model: "This protocol validates balance deltas on all incoming funds"
• Used this model to judge all paths as secure
• The fillOrder path, which lacked validation, wasn't included in the model
• Result: Vulnerability missed due to model incompleteness

Codex's Approach:

• Did not construct a global model
• Checked each path individually
• Marked ammHandleTransfer's inflow as "assumed to already be sitting in the handler"
• Didn't concern itself with whether other paths had protection
• Result: Vulnerability identified through systematic path-by-path verification

This case demonstrates a fundamental truth: constraining Claude's context to only business flow-related functions may cause it to overgeneralize conclusions from one flow to the entire protocol. Paradoxically, context constraints designed to help Claude focus may actually limit its global reasoning capability.

EVMbench Data: Empirical Evidence

The EVMbench benchmark provides quantitative support for these observations:

Claude excels at detection tasks requiring holistic comprehension, while Codex dominates exploitation tasks demanding precise path construction and iterative debugging—precisely the capabilities optimized by its reinforcement learning training.

Semgrep Research: Complementary Insights

Independent research from Semgrep offers additional validation:

• Claude: Reported 2x more findings (46 vs 21) but with higher false positive rate (86% vs 82%)
• Claude: Stronger at IDOR vulnerabilities (requiring business semantic understanding)
• Codex: Superior at Path Traversal (requiring step-by-step data flow tracking)
• Codex: Higher variance across multiple runs (less deterministic)

These findings align perfectly with the cognitive model analysis: global understanding leads to higher recall but also higher false positives; step-by-step execution yields lower recall but higher precision.

Strategic Skill Design: Adapting to Model Characteristics

Understanding these fundamental differences enables us to design audit skills that leverage each model's strengths while mitigating weaknesses.

Principle 1: Different Design Philosophies

For Claude: Guidance and Constraint

• Prevent step-skipping behavior
• Stop inference from replacing verification
• Guard against distraction by irrelevant information
• Force questioning language rather than declarative statements

For Codex: Connection and Context

• Help it see relationships between steps
• Enable combination of isolated findings into attack chains
• Provide global context explicitly
• Create detailed, executable task lists

Principle 2: Project Pattern Recognition

The business analysis phase produces documentation that serves as the "worldview" for all subsequent vulnerability mining agents. This isn't merely information—it's a framework determining what questions agents will ask and what they'll ignore.

Critical Insight: If the business analysis phase describes an unverified assumption as fact, no subsequent agent will question it.

In A/B testing with the same callback function's fund inflow:

• Version A: Described as "Protocol receives X tokens" (declarative) → All agents treated as verified
• Version B: Described as "Assuming X tokens have arrived" (questioning) → Agents actively verified actual received amounts

This single wording difference determined whether a High-severity vulnerability was discovered.

Design Implications:

• For Claude: Phase 1 instructions must强制 use questioning language. Every assumption not explicitly verified in code must be labeled as assumption, not fact.
• For Codex: Business analysis must produce explicit connection information—which functions share state, which form transaction sequences, which invariants must persist across functions.

Principle 3: Matching Analysis Granularity to Natural Stopping Layers

Each model has a "natural stopping layer"—the level at which it tends to draw conclusions.

Claude's Natural Stopping Layer: Conceptual Level

• Concludes: "Has EIP-712 signature protection"
• Concludes: "Has reentrancy guard"
• Concludes: "Has balance check"
• Then stops digging deeper

Codex's Natural Stopping Layer: Task Completion Level

• Completes instructed steps, then stops
• If instructed to "check for signature verification," answers "yes" without追问 "which fields does the signature cover?"

Design Strategy: Push analysis requirements below the natural stopping layer.

For Claude: Provide explicit reasoning paths and questioning angles. Instead of asking "Is there signature protection?", ask "Which parameters does the signature cover, and which economically significant parameters are excluded?" Instead of "Is there a balance check?", ask "On this specific path, what is the data source for accounting amounts—is it actual balance measurement or nominal parameters?"

This explains Claude's excellent performance in paired auditing with experienced security researchers. The researcher provides analysis frameworks and questioning angles, while Claude serves as an extension of the researcher's thinking, enabling rapid and efficient problem resolution.

For Codex: Provide detailed, executable task lists. Don't ask "Analyze this function"—decompose into explicit subtasks with defined output formats. Codex will precisely execute each defined subtask, so your decomposition granularity determines analysis depth.

When paired with Codex, one experiences a "kick-and-move" sensation—it completes the tasks反馈 by the auditor without excessive extension or expansion.

Principle 4: Execution Instructions

When designing smart contract audit skills, we establish a framework and write execution instructions guiding the model's analysis operations. How should we design appropriate instructions?

The Fundamental Difference:

• Claude needs "must do": It understands methodology but executes selectively. If the dimensional methodology says "construct repeated attacks to calculate cumulative error," Claude understands but may skip this step if it judges single-step error as small. It treats methodology as guidelines, filtering steps through its own judgment.
• Codex needs "know what to do": It executes methodology without understanding intent. If instructed to "analyze this function," Codex executes but may not know what to focus on. If told to "check if parameter A is signature-covered," it checks precisely—but won't proactively追问 "which other parameters should also be checked?"

A rigorous, concretely executable audit process and methodology may execute more efficiently and controllably on Codex.

Principle 5: Multi-Agent Information Sharing

For skills using parallel multi-agent vulnerability mining, agent division of labor should adapt to model characteristics.

Guiding Principle: Put information requiring correlation into the same agent's context.

For Claude: Leverages association capability. Give each agent fewer functions but more analysis dimensions—allow free cross-dimensional association within a contract. Topology tends toward contract-based grouping.

For Codex: Doesn't spontaneously associate well. Let each agent focus on one analysis dimension but see all contracts—it can compare different contracts' handling under the same dimension ("Contract A does this check, does Contract B?"). Topology tends toward dimension-based grouping.

Common vulnerability pattern matching or checklist methods in contract audit skills seem better suited to Codex's execution habits.

This isn't an absolute rule but a default preference. If two contracts in your protocol have strong state coupling, even Codex should have them in the same agent.

Principle 6: Vulnerability PoC Verification

Claude's Natural Advantage: Causal Reasoning

• Excels at explaining "why this is a bug" and "what happens with/without this bug"
• Leverage this: Require Claude's PoC to include both "proof of vulnerability existence" and "proof of correct behavior" tests
• It excels at constructing such causal contrasts

Research from KatanaQuant reveals a striking finding: AI programming agents discover only 13% of production-critical bugs during self-review, but when directly asked "what could go wrong," they provide correct diagnosis 100% of the time.

Codex's Natural Advantage: Iterative Debugging

• Reinforcement training excels at "run → fail → diagnose → fix → retry" cycles
• Leverage this: Design PoC verification as test loops allowing multiple fix attempts
• Advanced technique: Have Codex write tests for each SAFE conclusion too. If it thinks "there's balance check protection," have it write a test using fee-on-transfer mock to prove this protection truly exists. If the test fails, the SAFE conclusion is automatically overturned.

This represents Codex's unique advantage: transforming static analysis into dynamic verification. Claude struggles with this because its thinking mode is "reasoning to conclusion" rather than "testing to verify conclusion."

Conclusion: Embracing the AI Wave

As AI capabilities evolve at a daily pace, the AI-ification of business processes sweeps forward like a tidal wave. We all find ourselves within this wave, simultaneously using AI to replace our own work while worrying about being replaced by AI. This naturally generates anxiety, resistance, and confusion.

However, when uncertain about the future path, the appropriate response is to maintain a mindset ready for constant adaptation. When the wave arrives, we should dive into it and surf, rather than desperately clinging to the reef.

The key insight from this analysis isn't that one model is universally better—it's that understanding each model's cognitive architecture enables us to design better audit skills, choose appropriate tools for specific tasks, and ultimately improve our security posture through intelligent human-AI collaboration.

The future of security auditing lies not in choosing between models, but in strategically leveraging their complementary strengths while remaining aware of their inherent limitations.