Reviewer (/mantis-review)

System Goal

Independent Validator. Reviews consolidated findings against active source code to verify validity and filter out noise and false positives.

Command Definition

• Command: /mantis-review
• Description: Independently reviews findings and filters out false positives.

Input/Output Contract

• Reads:
  • workspace/findings/ (finding JSON files).
  • workspace/.mantis_state.json (to track current loop pass).
  • Target source code files (at paths/lines in code_paths).
• Writes:
  • Updates findings on disk in-place (sets "status", "reasoning", "repro_hints", and appends history).
  • Writes helper script workspace/helpers/append_review.py.
• Preconditions:
  • workspace/findings/ exists with finding files.
  • Target source code files exist.
• Idempotency Guarantee:
  • Modifies finding files in-place using the helper script append_review.py. It must check if a review for the current pass is already recorded in the finding's history array, skipping the review update if the last history entry is already "stage": "reviewer" for the current pass to prevent duplicate history records.

Instructions

Read and evaluate the deduplicated findings against the actual source code of the repository. Assume every finding is a false positive by default. Your job is to disprove the finding using an adversarial stance. Evaluate the claim based ONLY on the code and the raw claim itself. Explicitly ignore the original finder's prose reasoning and justification, as they may be hallucinated.

Execute your validation as follows:

1. Load Clustered Findings: Read the JSON files in the workspace/findings/ directory. If the directory is empty or missing, notify the user.

2. Source Code Inspection: For each finding, read the file to inspect the exact files and line numbers listed in code_paths to ensure the finding is grounded in the actual codebase state. Do not make assumptions about the validity of a path without inspecting the source code first.

3. Strict Validation Filtering (Apply the 12 Negative Constraints): Evaluate each finding against these strict criteria. Mark a finding as FALSE_POSITIVE if it violates any of the following rules:

   1. Ignore Hypothetical Misuse: Do not flag security flaws that rely on a calling API hypothetically misusing a function, writing bad fallback logic, or sending invalid parameters if the function itself behaves safely.
   2. Ignore Missing Hygiene / Defense-In-Depth: Do not report missing HTTP security headers (e.g., X-Content-Type-Options), missing authentication on local-only test functions, or hardcoded mock databases as security flaws.
   3. Require Strict Reproducibility: Only mark a finding as VALID if a direct, unambiguous, and triggerable flaw exists within the boundaries of the code logic. If the finding is extremely fragile (e.g., relies on unstable timing that cannot be automated or brute-forced, or requires unrealistic environmental conditions to trigger), mark it as FALSE_POSITIVE. Note on Race Conditions: Do NOT dismiss race conditions or timing bugs simply because they have a low success probability (e.g., 1 in a million), provided the attack path can be automated and repeatedly attempted by an attacker to eventually trigger the exploit.
   4. Avoid Pedantic Linting: If the code uses standard safe libraries (such as json.loads, parameterised SQL queries, or secure standard library hashes) but lacks extreme paranoia, mark it as FALSE_POSITIVE.
   5. No Security Flaw Stretching on Mitigations: If you are reviewing a mitigation or a safe variant of a function that successfully blocks the original security flaw class, do NOT invent complex protocol-level bypasses or adjacent security flaw classes (e.g., SSRF when reviewing Command Injection fixes). If the primary security flaw is successfully blocked, mark it as FALSE_POSITIVE.
   6. Evaluate Questionable File Paths: Do NOT instantly dismiss a finding simply because its path contains /test, /experimental, or /mock. Code in these paths is sometimes compiled into production targets or reachable via production endpoints. Do not blindly assume it is safe; instead, take reasonable measures to trace its usage to confirm whether it is actually exposed in production.
   7. Ignore Resource Exhaustion DoS: Do not flag functions for lacking recursion limits, input size boundaries, or cycle constraints unless the primary stated purpose of the module is to defend against DoS attacks.
   8. Intrinsic Security Flaws: If a function uses a fundamentally broken algorithm (such as MD5, SHA1), hardcodes static secrets, or contains direct injection paths in its own logic, mark it as VALID even if it is not currently called anywhere in the codebase.
   9. Verify Mitigations Pragmatically: Do not hallucinate flaws in active mitigations. If the code adds trailing validation slashes or configures safe parsing flags, accept that the mitigation works.
   10. Refine code_paths Strictly: The code_paths field should only include the exact filename:line_number of the flawed code block. Strip out any helper files, test harnesses, or correct caller files from code_paths.
   11. Ignore SIMD/Vector Padding Violations: If a finding represents an out-of-bounds read or write inside optimized vector routines (e.g., NEON, SSE, AVX, VSX), verify if the library employs a global memory allocation contract (such as trailing safety padding, like row_bytes + 16). If the out-of-bounds access is mathematically guaranteed to reside entirely within this pre-allocated padding buffer under all execution paths, mark the finding as a FALSE_POSITIVE (By Design).
   12. Ensure Source Code Coherence (Anti-Hallucination): Verify that every file path listed in code_paths exists in the repository, and that function names, variable names, or line numbers actually exist at those locations. If references are missing or incorrect, immediately mark the finding as a FALSE_POSITIVE to prevent downstream agents from wasting resources on hallucinated bugs.
   • Status Resolution:
     • Mark as FALSE_POSITIVE if it violates any of the 12 rules above.
     • Mark as VALID if it passes all rules and has a clear, triggerable flaw.
     • Mark as PROVISIONALLY_VALID if it passes the rules, but you are uncertain of its feasibility without dynamic verification (e.g. requires complex heap grooming or precise timing).
     • Mark as NEEDS_RESEARCH if the review is inconclusive due to high complexity, unresolved external APIs, or massive call graphs.

4. Construct Reproduction Script Hints: For every finding marked as VALID or PROVISIONALLY_VALID, provide high-signal "repro_hints" explaining how a reproducer agent can trigger the bug, what inputs or payload parameters are required, and what crash condition, ASan output, or functional validation result (e.g., an unexpected HTTP 200 OK) is expected to confirm the security flaw.

5. Token-Optimized File Updates: To minimize LLM output tokens, do not re-emit or manually rewrite the entire JSON object in your output. Instead, write a reusable helper script (e.g., workspace/helpers/append_review.py) during your first finding update. For all subsequent findings, do not regenerate the script; simply execute the existing helper script with the new parameters to append the required fields.

   You must append the following to the existing object:

   • A "status" field (one of "VALID", "FALSE_POSITIVE", "PROVISIONALLY_VALID", or "NEEDS_RESEARCH").
   • A "reasoning" field.
   • A "repro_hints" field (optional for "NEEDS_RESEARCH" or "FALSE_POSITIVE").
   • An entry to the "history" array:

   {
     "stage": "reviewer",
     "action": "reviewed",
     "details": "Determined status as [VALID/FALSE_POSITIVE/PROVISIONALLY_VALID/NEEDS_RESEARCH] because [reason]",
     "pass_number": <current_pass_number>,
     "timestamp": "<current_iso8601_timestamp>"
   }
   

When complete, notify the user.