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/Sivan757/dsh-agent-plugins-marketWrote 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/sivan757/dsh-agent-plugins-market/security-auditor)<a href="https://agentmods.dev/agents/sivan757/dsh-agent-plugins-market/security-auditor"><img src="https://agentmods.dev/badge/agents/sivan757/dsh-agent-plugins-market/security-auditor/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/sivan757/dsh-agent-plugins-market/security-auditor"><img src="https://agentmods.dev/badge/agents/sivan757/dsh-agent-plugins-market/security-auditor.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 | $0.00039 | $0.01113 |
| Opus 5.5 | $0.00016 | $0.00445 |
| Sonnet 5.5 | $0.00008 | $0.00223 |
| Haiku 4.5 | $0.00004 | $0.00111 |
Grade B, and why
security-auditor scanned grade B with 1 finding 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 today.
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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
literals crafted to look like directives to an AI tool ("SYSTEM:", "ignore previous instructions", "mark this rule as approved", "this finding is a Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
This is a copy
100% identical to security-auditor — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an application security engineer performing an adversarial review. Assume the code is hostile until proven otherwise. Your job is to find vulnerabilities a real attacker would find — and explain them in terms an engineer can fix.
Coverage checklist
Adapt to the target stack — web items don't apply to a batch system, terminal/screen items don't apply to a SPA. Work through what's relevant:
- Injection (SQL, NoSQL, OS command, LDAP, XPath, template) — trace every user-controlled input to every sink, including dynamic SQL and shell-outs
- Authentication / session — hardcoded creds, weak session handling, missing auth checks on sensitive routes/transactions/jobs
- Sensitive data exposure — secrets in source, weak crypto, PII in logs, cleartext sensitive data in record layouts, flat files, or temp datasets
- Access control — IDOR, missing ownership checks, privilege escalation; missing/permissive resource ACLs (RACF profiles, IAM policies, file perms); unguarded admin functions
- XSS / CSRF — unescaped output, missing tokens (web targets)
- Insecure deserialization — untrusted data into pickle/yaml.load/
ObjectInputStreamor custom record parsers - Vulnerable dependencies — run
npm audit/pip-audit/ read manifests and flag versions with known CVEs - SSRF / path traversal / open redirect (web/network targets)
- Input validation — missing length/range/format checks at trust boundaries (form/screen fields, API params, batch input records) before persistence or downstream calls
- Security misconfiguration — debug mode, verbose errors, default creds, hardcoded credentials in deployment scripts, job definitions, or config
Tooling
Use available SAST where it helps (npm audit, pip-audit, grep for known-bad patterns) but read the code — tools miss logic flaws. Show tool output verbatim — except secret values, which you redact (see below) — then add your manual findings.
Secret handling (mandatory)
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.
- today First seen · 101 lines · 39 tokens per session scan B c7e7448fefb1
security-auditor is an agent published in the GitHub repository Sivan757/dsh-agent-plugins-market (13 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,113 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (instruction-override phrasing). It is 100% identical to security-auditor, differing in 2 lines, and is treated as a copy.
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