Who Tests the Testers? Systematic Enumeration and Coverage Audit of LLM Agent Tool Call Safety

2026-03-20T08:51:52Z3e8d2da44754c1ea97ed16c803ff8e02163980c7f400d623d604e34f4df7a37d
Claude CodeDirect Preference Optimization (DPO)GitHub CopilotGreen AILLM agent safetyLLM-assisted code reviewRust verificationSQL comment generationSafeAuditSpaceTime ProgrammingVCoT-Benchadversarial PRsautomated theorem provingbenchmark coverageconfirmation biasdebiasingdesign discussion detectionomniscient debugging and tracing tools","BenchBrowser","benchmarrepository miningrule-resistancesoftware supply-chain attackssustainable ML practicestransformer modelsverification chain-of-thoughtvulnerability detection

What happened

This collection of recent CS/SE preprints highlights multiple security-relevant findings around LLMs and developer tooling. SafeAudit introduces an LLM-driven enumerator and a non-semantic metric (“rule-resistance”) to meta-audit LLM agent tool-call safety, finding >20% residual unsafe behaviors across benchmarks and environments and showing coverage improves with more tests. A separate study on LLM-assisted security code review demonstrates strong confirmation bias: framing a change as bug-free reduces vulnerability detection by 16–93%, and adversarial pull requests can reintroduce known CVEs

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_se
Record identifier
3e8d2da44754c1ea97ed16c803ff8e02163980c7f400d623d604e34f4df7a37d
Enrichment time
2026-03-20T08:51:52Z
AI-assisted enrichment
Yes

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