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.
npx agentmods add skills/thejefflarson/soundcheck/rag-securitynpx skills add thejefflarson/soundcheck --skill rag-securitygit clone --depth 1 https://github.com/thejefflarson/soundcheckWhat 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 | $0.00068 | $0.00696 |
| Opus 5 | $0.00034 | $0.00348 |
| Sonnet 5 | $0.00014 | $0.00139 |
| Haiku 4.5 | $0.00007 | $0.00070 |
Grade B, and why
rag-security 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 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.
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.
into LLM context. Attacker-controlled documents can override system instructions, Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RAG Pipeline Security (OWASP LLM01:2025)
What this checks
Prevents prompt injection through retrieved documents and uncontrolled content flooding into LLM context. Attacker-controlled documents can override system instructions, exfiltrate data, or manipulate model behavior when injected without guardrails.
Vulnerable patterns
- Retrieved document concatenated into the system prompt — retrieved content can override developer instructions.
- HTTP fetch of a caller-supplied or document-supplied URL with no domain allowlist — SSRF surface and attacker-controlled content into context.
- No length or token cap on retrieved content, allowing one document to consume the entire context window.
- Retrieved content mixed into the prompt with no delimiter or trust label distinguishing it from developer instructions.
Fix immediately
Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties. Translate each property into the audited file's language, HTTP client, and LLM API — use the documented secure primitives of that stack.
- Retrieval sources are validated against a domain allowlist before fetch.
Arbitrary URLs from user input or from another document's links lead to SSRF
and to attacker-controlled documents landing in the context; the allowlist
is the same property enforced by the
ssrfskill for outbound HTTP. - Retrieved content is truncated to a fixed character or token cap before injection into the prompt. Unbounded retrieval lets a single document eat the context window — either denial of service or a vehicle for flooding instructions.
- Retrieved content is wrapped in explicit delimiters that label it as
untrusted data, and lives in the user role — never concatenated into the
system prompt. The model is more likely to treat it as data rather than
instructions when the framing is structural. See the
prompt-injectionskill for the trust-tier pattern. - Every retrieval is logged with source URL and content length — useful for incident response and for detecting poisoning attempts (sudden spikes in retrieved size or novel sources).
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.
- 2d ago First seen · 63 lines · 68 tokens per session scan B 6fa4f764ef19
rag-security is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 696 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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