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.
Use when deciding whether source and live reproduction evidence establish a new regression or an exclusion.
Review the instructions the agent will receive before you install.
Input: candidate, frozen sources, available reproduction/replay packages and registry assignment. Use a fresh context distinct from the reproducer and replayer. Early exclusions may lack experiments. Follow project guidance before target work.
Producing missing evidence; return the gap to the caller.
UNRESOLVED; state what remains
unproved. For UNRESOLVED, put the standard's concise paragraph separating observed gaps,
plausible explanations and resolving checks in reason so reports retain it.
Incomplete fixes and existing bugs stay outside regression totals.
Check known findings/issues/PRs before claiming novelty; report unknown if unavailable.decision.json; cite evidence for every conclusion.
Record any blocker preventing adjudication.Keep detailed reasoning in the sealed package. Return only:
complete or blocked, matching the record.decision.json path, or why none was produced.A completed decision may be UNRESOLVED; task status and finding outcome differ.
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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.
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.
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.
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.
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.
Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.