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Fp Check

Trail of BitsOfficialPublished by Trail of Bits

  • Skill
  • Review and audit
  • Security
  • CC-BY-SA-4.0
  • No issues found

From the author

Systematically verifies suspected security bugs to eliminate false positives, producing a TRUE POSITIVE or FALSE POSITIVE verdict with documented evidence for each. Use when asked whether a specific finding is real, exploitable, or a false positive, or to verify or validate a suspected vulnerability — not for hunting or discovering new bugs.
— the description by Trail of Bits

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

fp-check provides a structured methodology for validating suspected security bugs. It guides the user through a rigorous verification process to determine if a finding is a true positive or a false positive, supported by documented evidence. The skill distinguishes between standard and deep verification paths based on the complexity of the bug, ensuring that each claim is thoroughly analyzed against its specific context, data flow, and threat model.

What it does

  • Restates and clarifies vulnerability claims to identify potential misunderstandings.
  • Routes verification tasks between standard and deep paths based on bug complexity.
  • Performs data flow analysis to trace inputs from source to sink.
  • Applies class-specific verification requirements for various bug types.
  • Produces a final verdict of TRUE POSITIVE or FALSE POSITIVE with supporting evidence.
  • Evaluates findings against mandatory gate reviews.

When to use it

  • When you need to verify if a specific security finding is real or exploitable.
  • When you need to confirm if a reported bug is a false positive.
  • When you have a clear, specific vulnerability claim that requires validation.
  • When standard verification steps are insufficient for complex or cross-component bugs.

Advantages

  • Eliminates guesswork by requiring evidence-based verification.
  • Prevents common analytical errors through a structured devil's advocate review.
  • Provides a clear, repeatable process for triaging multiple suspected bugs.
  • Ensures context-aware analysis by considering architectural and historical factors.

Usage examples

“Verify if the reported heap buffer overflow in parse_header is a true positive.”
The agent will restate the claim, perform data flow analysis, check the specific memory corruption conditions, and provide a verdict with evidence.
“Is this SQL injection finding a false positive?”
The agent will trace the data flow from the input source to the database sink and verify if the input is properly sanitized or parameterized.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/trailofbits/fp-check/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip fp-check.zip -d .agents/skills

Documentation by Trail of Bits

False Positive Check

When to Use

  • "Is this bug real?" or "is this a true positive?"

  • "Is this a false positive?" or "verify this finding"

  • "Check if this vulnerability is exploitable"

  • Any request to verify or validate a specific suspected bug

When NOT to Use

  • Finding or hunting for bugs ("find bugs", "security analysis", "audit code")

  • General code review for style, performance, or maintainability

  • Feature development, refactoring, or non-security tasks

  • When the user explicitly asks for a quick scan without verification

Rationalizations to Reject

If you catch yourself thinking any of these, STOP.

RationalizationWhy It's WrongRequired Action
"Rapid analysis of remaining bugs"Every bug gets full verificationReturn to task list, verify next bug through all phases
"This pattern looks dangerous, so it's a vulnerability"Pattern recognition is not analysisComplete data flow tracing before any conclusion
"Skipping full verification for efficiency"No partial analysis allowedExecute all steps per the chosen verification path
"The code looks unsafe, reporting without tracing data flow"Unsafe-looking code may have upstream validationTrace the complete path from source to sink
"Similar code was vulnerable elsewhere"Each context has different validation, callers, and protectionsVerify this specific instance independently
"This is clearly critical"LLMs are biased toward seeing bugs and overrating severityComplete devil's advocate review; prove it with evidence

Step 0: Understand the Claim and Context

Before any analysis, restate the bug in your own words. If you cannot do this clearly, ask the user for clarification. Half of false positives collapse at this step — the claim doesn't make coherent sense when restated precisely.

Document:

  • What is the exact vulnerability claim? (e.g., "heap buffer overflow in parse_header() when content_length exceeds 4096")

  • What is the alleged root cause? (e.g., "missing bounds check before memcpy at line 142")

  • What is the supposed trigger? (e.g., "attacker sends HTTP request with oversized Content-Length header")

  • What is the claimed impact? (e.g., "remote code execution via controlled heap corruption")

  • What is the threat model? What privilege level does this code run at? Is it sandboxed? What can the attacker already do before triggering this bug? (e.g., "unauthenticated remote attacker vs privileged local user"; "runs inside Chrome renderer sandbox" vs "runs as root with no sandbox")

  • What is the bug class? Classify the bug and consult bug-class-verification.md for class-specific verification requirements that supplement the generic phases below.

  • Execution context: When and how is this code path reached during normal execution?

  • Caller analysis: What functions call this code and what input constraints do they impose?

  • Architectural context: Is this part of a larger security system with multiple protection layers?

  • Historical context: Any recent changes, known issues, or previous security reviews of this code area?

Route: Standard vs Deep Verification

After Step 0, choose a verification path.

Standard Verification

Use when ALL of these hold:

  • Clear, specific vulnerability claim (not vague or ambiguous)

  • Single component — no cross-component interaction in the bug path

  • Well-understood bug class (buffer overflow, SQL injection, XSS, integer overflow, etc.)

  • No concurrency or async involved in the trigger

  • Straightforward data flow from source to sink

Follow standard-verification.md. No task tracking — work through the linear checklist sequentially, documenting findings inline.

Deep Verification

Use when ANY of these hold:

  • Ambiguous claim that could be interpreted multiple ways

  • Cross-component bug path (data flows through 3+ modules or services)

  • Race conditions, TOCTOU, or concurrency in the trigger mechanism

  • Logic bugs without a clear spec to verify against

  • Standard verification was inconclusive or escalated

  • User explicitly requests full verification

Follow deep-verification.md. Track each phase as a task with explicit dependencies, and execute the phases using the plugin's analysis agents.

Default

Start with standard. Standard verification has two built-in escalation checkpoints that route to deep when complexity exceeds the linear checklist.

Batch Triage

When verifying multiple bugs at once:

  1. Run Step 0 for all bugs first — restating each claim often collapses obvious false positives immediately

  2. Route each bug independently (some may be standard, others deep)

  3. Process all standard-routed bugs first, then deep-routed bugs

  4. After all bugs are verified, check for exploit chains — findings that individually failed gate review may combine to form a viable attack

Final Summary

After processing ALL suspected bugs, provide:

  1. Counts: X TRUE POSITIVES, Y FALSE POSITIVES

  2. TRUE POSITIVE list: Each with brief vulnerability description

  3. FALSE POSITIVE list: Each with brief reason for rejection

References

  • Standard Verification — Linear single-pass checklist for straightforward bugs

  • Deep Verification — Full task-based orchestration for complex bugs

  • Gate Reviews — Six mandatory gates and verdict format

  • Bug-Class Verification — Class-specific verification requirements for memory corruption, logic bugs, race conditions, integer issues, crypto, injection, info disclosure, DoS, and deserialization

  • False Positive Patterns — 13-item checklist and red flags for common false positive patterns

  • Evidence Templates — Documentation templates for data flow, mathematical proofs, attacker control, and devil's advocate reviews

Files

references/standard-verification.md

Standard Verification

Linear single-pass checklist for straightforward bugs. No task tracking — work through each step sequentially and document findings inline.

Escalation Checkpoints

Two points in this checklist may trigger escalation to deep-verification.md:

  1. After Step 1 (Data Flow): Escalate if 3+ trust boundaries, callbacks/async control flow, or ambiguous validation chain

  2. After Step 5 (Devil's Advocate): Escalate if any question produces genuine uncertainty you cannot resolve

When escalating, hand off all evidence gathered so far — deep verification will continue from where you left off.

Checklist

Step 1: Data Flow

Trace data from source to the alleged vulnerability sink.

  • Map trust boundaries crossed (internal/trusted vs external/untrusted)

  • Identify all validation and sanitization between source and sink

  • Check API contracts — many APIs have built-in bounds protection that prevents the alleged issue

  • Check for environmental protections (compiler, runtime, OS, framework) that prevent exploitation entirely (not just raise the bar)

  • Apply class-specific checks from bug-class-verification.md

Key pitfall: Analyzing the vulnerable code in isolation. Conditional logic upstream may make the vulnerability mathematically unreachable. Trace the full validation chain.

Escalation check: If you found 3+ trust boundaries, callbacks or async control flow in the path, or an ambiguous validation chain — escalate to deep verification.

Step 2: Exploitability

Prove the attacker can trigger the vulnerability.

  • Attacker control: Prove the attacker controls data reaching the vulnerable operation. Internal storage set by trusted components is not attacker-controlled.

  • Bounds proof: For integer/bounds issues, create an explicit algebraic proof using the template in evidence-templates.md. Verify: IF validation_check_passes THEN bounds_guarantee_holds.

  • Race feasibility: For race conditions, prove concurrent access is actually possible. Single-threaded initialization and synchronized contexts cannot have races.

Step 3: Impact

Determine whether exploitation has real security consequences.

  • Distinguish real security impact (RCE, privesc, info disclosure) from operational robustness issues (crash recovery, cleanup failure)

  • Distinguish primary security controls from defense-in-depth. Failure of a defense-in-depth measure is not a vulnerability if primary protections remain intact.

Step 4: PoC Sketch

Create a pseudocode PoC showing the attack path. Executable and unit test PoCs are optional for standard verification.

Data Flow: [Source] → [Validation?] → [Transform?] → [Vulnerable Op] → [Impact]
Attacker controls: [what input, how]
Trigger: [pseudocode showing the exploit path]

See evidence-templates.md for the full PoC template.

Step 5: Devil's Advocate Spot-Check

Answer these 7 questions. If any produces genuine uncertainty, escalate to deep verification.

Against the vulnerability:

  1. Am I seeing a vulnerability because the pattern "looks dangerous" rather than because it actually is? (pattern-matching bias)

  2. Am I incorrectly assuming attacker control over trusted data? (trust boundary confusion)

  3. Have I rigorously proven the mathematical condition for vulnerability can occur? (proof rigor)

  4. Am I confusing defense-in-depth failure with a primary security vulnerability? (defense-in-depth confusion)

  5. Am I hallucinating this vulnerability? LLMs are biased toward seeing bugs everywhere — is this actually real or am I pattern-matching on scary-looking code? (LLM self-check)

For the vulnerability (always ask — false-negative protection):

  1. Am I dismissing a real vulnerability because the exploit seems complex or unlikely?

  2. Am I inventing mitigations or validation logic that I haven't verified in the actual source code? Re-read the code after reaching a conclusion.

Escalation check: If any question above produces genuine uncertainty you cannot resolve with the evidence at hand — escalate to deep verification.

Step 6: Gate Review

Apply all six gates from gate-reviews.md and all 13 items from false-positive-patterns.md to reach a verdict.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Trail of Bits
Publisher
Trail of Bits (Official)
Source
trailofbits/skills · plugins/fp-check/skills/fp-check · commit e4dd0bb
Licence
Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Trail of Bits or Trail of Bits.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download fp-check.zip