Independently rerun supplied proof-of-concept code against freshly built versions from before and after a change. Compare observed behavior, verify reachability through real interfaces, and preserve separate logs showing agreement, divergence, or blockers.
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Use when independently verifying a supplied PoC against fresh live before/after deployments.
安装前可以查看交给 AI 代理的指令内容。
Input: candidate, frozen source records, complete sealed reproduction package and registry assignment. Use a fresh context distinct from the reproducer. Follow project guidance before target work.
Authoring or repairing the PoC. Return changes to the caller for reproduction; changed PoCs require new reproduction and replay.
build_dirs. Execute the
unchanged PoC with the same input against both. Record whether S0 behaves
correctly and S1 exhibits the claimed failure.replay.json, referencing the reproduction record and original
PoC/input. Record divergence or blockers; uncertainty stays unresolved.
Release temporary resources without deleting evidence.Keep detailed evidence in the sealed package. Return only:
complete or blocked, matching the record.replay.json path, or why none was produced.Completion means the replay attempt finished, not that the regression is confirmed.
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Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.
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Assess source code and live reproduction evidence to decide whether a change introduced a regression, a newly caused defect. Record supported conclusions, exclusions, severity, and unresolved gaps in an evidence-linked decision file.
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Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.
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Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.
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Reviews Algorand smart contracts written in TEAL or PyTeal for 11 vulnerability patterns. Produces a complete coverage table and findings with code references, severity ratings, and suggested fixes, with optional automated checks using Tealer.
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Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.
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