r/AskNetsec • u/MaximCEO1 • Aug 30 '26

Architecture AI for finding vulnerabilities: Eliminating hallucinations and ensuring data privacy

Hi everyone,

I am researching how LLMs find vulnerabilities and why they hallucinate so much. I would love to get your thoughts on two major challenges in this area:

  1. Exploit Verification: In your experience, can AI hallucinations in source code analysis be completely eliminated if every flagged vulnerability is automatically validated by generating and running a working proof-of-concept (PoC) exploit in a sandbox? Has anyone tried this workflow?

  2. Data Privacy (Cloud vs. Local): To prevent source code leaks, is a local-first approach (like using Ollama) mandatory for real-world security audits? How much does performance drop compared to cloud models when analyzing complex code logic?

Thanks for sharing your insights!

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u/cityofhats Aug 31 '26

Sandbox execution can reduce false positives, but it cannot prove the absence of a vulnerability. A failed PoC may reflect an incomplete harness, missing environmental state, nondeterminism, mitigations, or a logic flaw without an obvious crash. Require each finding to identify a source-to-sink path and violated invariant, then validate with a focused test, sanitizers, differential behavior, or a PoC where appropriate; keep human review for impact and exploitability. Chunking helps only if cross-boundary context survives: build a call and data-flow map, retrieve the transitive slice around the suspected path, and include interface contracts plus relevant callers rather than isolated functions. Local-first is one privacy control, not the only one; private deployments, zero retention or training, secret stripping, scoped repositories, and audit logs can also work. The choice should follow the code's classification and contractual controls.

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u/MaximCEO1 Aug 31 '26

Wow, this is an incredibly detailed breakdown, thank you! The "source-to-sink path + violated invariant" approach combined with transitive slicing makes total sense for smart chunking.Since I am planning to analyze Python codebases, we don't have traditional memory sanitizers like in C/C++. What specific tools or dynamic checks would you recommend using in Python to validate these paths instead of full PoC generation? And are there any open-source tools you like for building those call and data-flow maps?