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Fp Check

Community
vibeeval
fp-check

Systematic false positive verification for security findings. Provides structured methodology to confirm or dismiss scanner results, manual audit findings, and automated alerts. Adapted from Trail of Bits. Use when triaging security scan results or verifying audit findings.

Overview

Publishervibeeval
Repositoryvibecosystem
Skill namefp-check
Stars
533
Forks
44
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vibeeval on GitHub. Read the source before you install it.

Installation

Install the Fp Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vibeeval/vibecosystem/tree/main/skills/fp-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vibeeval/vibecosystem.git /tmp/vibecosystem
mkdir -p .claude/skills
cp -r /tmp/vibecosystem/skills/fp-check .claude/skills/fp-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fp Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fp Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fp Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

False Positive Verification

Not every finding is real. But dismissing a real finding as "false positive" is worse than investigating a false one. This skill provides a systematic approach to verify findings without bias.

Verification Process

Step 1: Reproduce the Claim

Before dismissing anything, attempt to confirm:

FINDING: SQL injection in /api/users
CLAIM: User input reaches database query unsanitized

VERIFICATION:
1. Read the actual code at the reported location
2. Trace the data flow from input to sink
3. Check for sanitization/validation between input and sink
4. Check for framework-level protections (ORM, parameterized queries)
5. Attempt to construct an exploit payload

Step 2: Evidence-Based Triage

VerdictCriteriaEvidence Required
TRUE POSITIVEVulnerability exists and is exploitableCode path + exploit scenario
TRUE POSITIVE (mitigated)Vulnerability exists but other controls prevent exploitationCode path + mitigation proof
FALSE POSITIVE (provable)Finding is wrong due to tool limitationSpecific reason why tool was wrong
FALSE POSITIVE (contextual)Code is technically flagged but context makes it safeContext documentation
NEEDS INVESTIGATIONCannot determine without more analysisWhat additional info is needed

Step 3: Document the Decision

FINDING: [scanner/auditor finding description]
SOURCE: [which tool/person reported it]
LOCATION: file.ts:42

VERDICT: [TRUE POSITIVE | FALSE POSITIVE | NEEDS INVESTIGATION]

EVIDENCE:
  - [What you checked]
  - [What you found]
  - [Why you reached this conclusion]

REASONING:
  [Detailed explanation of why this is/isn't a real finding]

CONFIDENCE: [HIGH | MEDIUM | LOW]
  [If LOW, explain what would increase confidence]

Common False Positive Patterns

1. Scanner Doesn't Understand Context

Scanner says: "Hardcoded password detected"
Actual code: const DEFAULT_LABEL = "password"
Verdict: FALSE POSITIVE -- it's a UI label, not a credential
Evidence: Variable is used only in form field label rendering

2. Framework Protection Not Recognized

Scanner says: "SQL injection in query"
Actual code: db.query("SELECT * FROM users WHERE id = $1", [userId])
Verdict: FALSE POSITIVE -- parameterized query prevents injection
Evidence: $1 is a parameter placeholder, userId is bound safely

3. Dead Code / Unreachable Path

Scanner says: "XSS in renderUserInput()"
Actual code: renderUserInput() exists but is never called
Verdict: FALSE POSITIVE -- function is dead code
Evidence: grep shows no callers; function should be removed anyway
WARNING: Verify it's truly unreachable, not just unused currently

4. Test Code Flagged

Scanner says: "Hardcoded API key"
Actual code: const TEST_KEY = "test-key-123" in test/fixtures.ts
Verdict: FALSE POSITIVE -- test fixture, not production code
Evidence: File is in test directory, key is clearly a test value
WARNING: Verify the key isn't a real key used in test environment

5. Intentional Behavior

Scanner says: "Insecure random number generation"
Actual code: Math.random() used for UI animation timing
Verdict: FALSE POSITIVE -- not used for security purposes
Evidence: Used only for visual jitter in animation, no security impact

Red Flags: When "False Positive" Is Actually Real

Do NOT dismiss if:

Red FlagWhy It Matters
"It's behind a VPN"VPNs get compromised, zero trust is the standard
"Only admins can reach it"Admin accounts get compromised
"The input is from our other service"Services can be compromised too
"We sanitize it elsewhere"Verify the "elsewhere" actually runs
"It's just a low severity"Low severity findings chain into high impact
"The scanner is always wrong about this"Verify EACH instance independently
"We've never been exploited"Survivorship bias

Verification Techniques

1. Data Flow Tracing

Follow the data from source to sink:

Source (user input) -> [validation?] -> [transformation?] -> [sanitization?] -> Sink (dangerous operation)

If ANY step is missing or bypassable, it's a TRUE POSITIVE.

2. Control Flow Analysis

Check all paths to the vulnerable code:

Can the code be reached without authentication?
Can the code be reached with different parameters?
Can the code be reached through an alternative route?

3. Exploit Attempt

Construct a minimal proof:

Input: [specific malicious input]
Expected: [what should happen if vulnerable]
Actual: [what actually happens]
Blocked by: [what prevents exploitation, if anything]

4. Historical Check

bash
# Has this code had real vulnerabilities before?
git log --grep="fix\|vuln\|security\|CVE" -- <file>

# Has the scanner been wrong about this pattern before?
# Check past triage decisions for this rule

Batch Triage Template

For large scan results:

markdown
# Security Scan Triage - [Date]

Scanner: [tool name and version]
Scan target: [repo/branch/commit]
Total findings: [N]

## Summary
| Verdict | Count |
|---------|-------|
| True Positive | X |
| True Positive (mitigated) | X |
| False Positive | X |
| Needs Investigation | X |

## True Positives (Action Required)
1. [SEVERITY] file.ts:42 -- [description] -- [recommended fix]

## False Positives (Documented)
1. file.ts:88 -- [reason it's false positive]

## Needs Investigation
1. file.ts:120 -- [what additional info is needed]

Integration with vibecosystem

  • security-reviewer agent: Use fp-check after running security scans
  • sast-scanner agent: Triage Semgrep results with this methodology
  • code-reviewer agent: When flagging potential issues, verify first
  • verifier agent: Include false positive check in quality gate

Inspired by Trail of Bits fp-check plugin.

Frequently asked questions

What does the Fp Check AI skill do?

Systematic false positive verification for security findings. Provides structured methodology to confirm or dismiss scanner results, manual audit findings, and automated alerts. Adapted from Trail of Bits. Use when triaging security scan results or verifying audit findings.

Why use Fp Check on TypingMind?

Because you install it once and use it with any model. Fp Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fp Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vibeeval/vibecosystem/tree/main/skills/fp-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fp Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fp Check?

As many as you like. As long as a model supports skills, you can use Fp Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fp Check AI skill free?

Yes. It is published on GitHub by vibeeval under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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