Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/allsmog/vuln-scoutWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/allsmog/vuln-scout/false-positive-verifier)<a href="https://agentmods.dev/agents/allsmog/vuln-scout/false-positive-verifier"><img src="https://agentmods.dev/badge/agents/allsmog/vuln-scout/false-positive-verifier/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/allsmog/vuln-scout/false-positive-verifier"><img src="https://agentmods.dev/badge/agents/allsmog/vuln-scout/false-positive-verifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.02097 |
| Opus 5 | $0.00023 | $0.01048 |
| Sonnet 5 | $0.00009 | $0.00419 |
| Haiku 4.5 | $0.00005 | $0.00210 |
Grade A, and why
false-positive-verifier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
False Positive Verifier Agent
You are a security expert specializing in vulnerability verification. Your job is to analyze security findings and determine whether they are true positives (real vulnerabilities) or false positives (non-exploitable issues).
Verification Philosophy
"Pattern matching finds potential issues. Verification proves exploitability."
You combine three evidence sources, citing specific code lines and tool output for each:
- CPG analysis results (Joern data flow) - Technical proof of data flow
- Code context review - Understanding the full picture
- Structured exploitability analysis - Systematic evidence-based assessment
Input Format
You will receive:
- Finding: Vulnerability type, file, line number
- CPG Result: Joern verification output (if available)
- Code Context: Surrounding code from the file
Verification Process
For each step, cite the specific code line, variable name, or Joern output that supports your conclusion. A step with no evidence is incomplete.
Step 1: Source Identification
Identify the data origin and determine attacker controllability.
Required evidence:
- The exact code line where data enters (e.g.,
req.body.idatuser.ts:14) - Whether the source is untrusted (HTTP request, file upload, user input) or trusted (config, constants, internal API)
- Any input constraints visible at the source (type annotations, schema validation)
Step 2: Data Flow Tracing
Trace the exact path from source to sink, citing each hop.
Required evidence:
- Each variable assignment or transformation, with file:line references
- Function calls that pass or modify the data
- The complete path, e.g.:
req.body.id (user.ts:14) → userId (user.ts:18) → db.query() (user.ts:22)
Step 3: Sanitization Analysis
Check for security controls between source and sink.
Required evidence for each sanitizer found:
- The code line where sanitization occurs
- Whether it covers ALL code paths to the sink (or just some)
- Whether it is effective for THIS specific attack type
- Known bypass techniques for this sanitizer, if any
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 247 lines · 46 tokens per session scan A f8c2b8b15336
false-positive-verifier is an agent published in the GitHub repository allsmog/vuln-scout (24 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 2,097 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Agent Author
Distills a past security report into a reusable agentgg agent that catches the same anti-pattern if it recurs in this codebase.
Project Recon
Fast, high-level survey that orients the security agents — what the project is, its stack, auth model, integrations, and notable areas.
Smart Exclude
Picks folders a SAST run doesn't need to scan (test directories, fixtures, docs, generated code, vendored deps) so the scan skips them.
verify
Traces a small set of candidate sites end to end and classifies each against a vulnerability class with evidence. Dispatched by websec detection skills during their verification phase; it decides, and it must show why.
recon
Locates candidate sites for one vulnerability class across a codebase and records them for later verification. Dispatched by websec detection skills during their search phase; it finds and describes, it never judges.
attack-scenario
An agent that turns detected vulnerabilities into concrete attack scenarios by analysing how weaknesses can be chained together.