Unverified50% confidenceFactExact time
LLM-based code auditing is limited by the context window, may produce hallucinated false positives, and struggles to guarantee reproducible results.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
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Related Claims
UnverifiedLLM-based code auditing has difficulty performing precise state tracking across files and cross-function call chains, and cannot actually execute code to discover runtime vulnerabilities the way fuzz testing can.77% similarUnverifiedIn data pipeline debugging scenarios, LLM hallucinations directly manifest as incorrect root cause identification, potentially misleading engineers in their troubleshooting.73% similarUnverifiedIn code generation scenarios, non-determinism may cause the same prompt to sometimes generate the correct bounds check and sometimes omit it, posing a risk to software engineering processes that require auditing and reproducibility.72% similarVerifiedHallucination problems in LLM code generation manifest as fabricating non-existent APIs, incorrectly combining parameter orders of real APIs, or generating deprecated usage based on outdated documentation.71% similarUnverifiedLLMs do not maintain a factual database and lack the metacognitive ability to know 'whether an operation was actually performed'; when the context strongly implies a certain output pattern, they generate text conforming to that pattern with high confidence, even if the content is entirely fabricated.71% similar
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