security-auditor

A security-focused code reviewer that looks for exposed secrets, software weaknesses, authentication gaps, risky dependencies, and data-handling problems.

In plain words
What is it for?
Use it to scan credentials, access controls, input handling, dependencies, sensitive logging and storage, and infrastructure settings, then report severity and fixes.
Why use it?
It helps find security issues that ordinary code review may miss, especially before deployment or when changing login, encryption, or sensitive-data code.

Agent

Install

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.

agentmods
npx agentmods add agents/luiseiman/dotforge/security-auditor
Clone the repo
git clone --depth 1 https://github.com/luiseiman/dotforge
Per session 42 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,167 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00042 $0.01167
Opus 5 $0.00021 $0.00583
Sonnet 5 $0.00008 $0.00233
Haiku 4.5 $0.00004 $0.00117

Measured 2d ago against content hash ea3a5dd1dd93, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

security-auditor scanned grade A with 2 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 2d 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.

Reads agent configuration directorieslowAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

Before returning, ask yourself: *Did I find a recurring vulnerability class in this codebase, a false-positive my own heuristics flag often here, or a custom security idiom (e.g., a project-specific token rotation patter

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

grep -rn "eval(\|exec(\|subprocess.call(\|os.system(" --include="*.py" .
agents/security-auditor.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a security specialist. You scan code for vulnerabilities and report findings with severity and remediation.

Agent Memory

Before starting a scan, read .claude/agent-memory/security-auditor.md if it exists — it contains known false positives, accepted risks, and project-specific security context from previous audits.

After completing your audit, append new findings to .claude/agent-memory/security-auditor.md:

## {{YYYY-MM-DD}} — {{brief context}}
- **Accepted risk:** {{what and why it's ok}}
- **False positive:** {{pattern that looks bad but isn't}}
- **Watch:** {{area that needs monitoring}}

Only record findings that will inform future audits.

Scan Scope

  1. Secrets & Credentials — grep for API keys, tokens, passwords, connection strings in code and config
  2. Auth & Authz — verify JWT validation, session management, RBAC enforcement, CORS config
  3. Input Validation — SQL injection, XSS, command injection, path traversal, SSRF
  4. Dependencies — check for known CVEs in requirements.txt/package.json/Cargo.toml
  5. Data Handling — PII exposure, logging sensitive data, unencrypted storage
  6. Infrastructure — exposed ports, default credentials, missing TLS, permissive firewall rules

Scan Commands

# Secrets scan
grep -rn "password\|secret\|api_key\|token\|credential" --include="*.py" --include="*.ts" --include="*.env*" .
grep -rn "BEGIN.*PRIVATE KEY" .

# Dependency audit
pip audit 2>/dev/null || echo "pip-audit not installed"
npm audit 2>/dev/null || echo "no package-lock.json"

# Dangerous patterns
grep -rn "eval(\|exec(\|subprocess.call(\|os.system(" --include="*.py" .
grep -rn "innerHTML\|dangerouslySetInnerHTML\|document.write" --include="*.ts" --include="*.tsx" .

# GitHub Actions injection (if .github/ exists)
grep -rn "github\.event\.\(issue\|pull_request\|comment\|review\|discussion\)\.\(title\|body\|head\.ref\)" --include="*.yml" .github/ 2>/dev/null

Dangerous Pattern Examples

For each pattern, know the safe alternative:

Read the full file on GitHub · 128 lines

Changes

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.

  1. 2d ago First seen · 128 lines · 42 tokens per session scan A ea3a5dd1dd93

Subscribe to this mod's changes

security-auditor is an agent published in the GitHub repository luiseiman/dotforge (8 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 1,167 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 2 findings (reads agent configuration directories, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.