Needle in the haystack LLMs for vulnerability research

[carlospolop]

🤖 Automated Content Update

This PR was automatically generated by the HackTricks News Bot based on a technical blog post.

📝 Source Information

• Blog URL: https://devansh.bearblog.dev/needle-in-the-haystack/
• Blog Title: Needle in the haystack: LLMs for vulnerability research
• Suggested Section: AI Security (LLM-assisted vulnerability discovery methodology) + Pentesting Web -> JWT Vulnerabilities (JWT/JWKS alg confusion) + Pentesting Web -> Authorization/IDOR (authz tier confusion pattern) + Generic Methodologies & Resources -> Threat Modeling (invariant-driven audits)

🎯 Content Summary

Needle in the haystack: LLMs for vulnerability research (technical summary)

The post explains a pragmatic methodology for using LLM agent CLIs/TUIs (explicitly mentioning OpenAI Codex) to discover real vulnerabilities in large codebases without manual source review. The core claim is that vulnerability discovery is a “needle-in-the-haystack” problem where the bug is often a single invariant violation hidden among large amounts of valid code, and that over-scaffolding<...

🔧 Technical Details

Threat-model-driven LLM auditing (minimal scaffolding): Build a compact, editable threat model by feeding the LLM previously disclosed CVEs/advisories for the project, then use it to enumerate entry points (HTTP/RPC/CLI/jobs), trust boundaries (browser↔server, service↔service, plugin↔host, sandbox↔privileged), and high-risk sinks (deserialization, templating, native bindings, authz checks, untrusted parsing). Derive invariants (“only admins can call X”, “JWT issuer must be Y”) and search for invariant violations and fix-bypasses in small, focused contexts to avoid long-context degradation (“context rot” / lost-in-the-middle effects). Verify suspected issues with concrete verifiers (tests, harnesses, sanitizer builds, fuzzers, static/grep invariant checks) rather than trusting model assertions.

Authorization tier confusion via incomplete privilege checks: When applications support multiple credential tiers (e.g., master vs read-only master), audit handlers that ga...

🤖 Agent Actions

Summary of changes:

• Added a focused section on threat-model-driven LLM audits with invariant-first methodology, concrete invariant patterns, and prompt frames.
• Appended the blog URL to the references.

Updated file:

• src/AI/AI-Assisted-Fuzzing-and-Vulnerability-Discovery.md

Notes:

• I did not add content to JWT or IDOR pages because the specific JWT/JWKS alg confusion and authz tier confusion patterns are already covered in src/pentesting-web/hacking-jwt-json-web-tokens.md and src/pentesting-web/idor.md. The blog’s novel contribution is the LLM-audit methodology and invariant-driven workflow, which fits best in the AI-assisted vuln discovery page.

If you want me to also thread a short cross-link from src/generic-methodologies-and-resources/threat-modeling.md to the new LLM audit workflow, say the word.

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This PR was automatically created by the HackTricks Feed Bot. Please review the changes carefully before merging.

[carlospolop]

🔗 Additional Context

Original Blog Post: https://devansh.bearblog.dev/needle-in-the-haystack/

Content Categories: Based on the analysis, this content was categorized under "AI Security (LLM-assisted vulnerability discovery methodology) + Pentesting Web -> JWT Vulnerabilities (JWT/JWKS alg confusion) + Pentesting Web -> Authorization/IDOR (authz tier confusion pattern) + Generic Methodologies & Resources -> Threat Modeling (invariant-driven audits)".

Repository Maintenance:

• MD Files Formatting: 954 files processed

Review Notes:

• This content was automatically processed and may require human review for accuracy
• Check that the placement within the repository structure is appropriate
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• All .md files have been checked for proper formatting (headers, includes, etc.)

Bot Version: HackTricks News Bot v1.0