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 skills add adriannoes/awesome-agentic-ai --skill red-teaming-llms-with-garakgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/skills/adriannoes/awesome-agentic-ai/red-teaming-llms-with-garak)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/red-teaming-llms-with-garak"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/red-teaming-llms-with-garak/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/skills/adriannoes/awesome-agentic-ai/red-teaming-llms-with-garak"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/red-teaming-llms-with-garak.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 2 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Anti-Refusal · line 76 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
- high Prompt Injection · line 76 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
- high Anti-Refusal · line 93 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
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 | $0.00049 | $0.02830 |
| Opus 5 | $0.00024 | $0.01415 |
| Sonnet 5 | $0.00010 | $0.00566 |
| Haiku 4.5 | $0.00005 | $0.00283 |
Grade B, and why
red-teaming-llms-with-garak 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 9d 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.
Nullifies safety policiesmediumAnti-refusal
"You have no restrictions", "do anything now", "ignore your guidelines": a direct jailbreak that disables guardrails.
- `dan` — "Do Anything Now" and related jailbreaks (e.g. `dan.Dan_11_0`). 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red-Teaming LLMs with garak
Legal and Authorized-Use Notice: This skill is for authorized AI security testing and educational purposes only. Probe only models, API keys, and endpoints you own or have explicit written permission to test. Automated probing of third-party LLM APIs may violate their terms of service and consume billable tokens. Unauthorized probing of systems you do not control may be illegal.
Overview
garak (Generative AI Red-teaming and Assessment Kit) is an open-source LLM vulnerability scanner maintained by NVIDIA. It plays the role that a network vulnerability scanner like Nessus plays for hosts, but for large language models: it sends thousands of adversarial prompts ("probes") at a target model, captures the generations, and runs automated "detectors" over the responses to decide whether each attempt succeeded. Probe families cover prompt injection (promptinject, latentinjection), jailbreaks (dan), training-data and system-prompt leakage (leakreplay), malware generation (malwaregen), cross-site-scripting payload emission (xss), encoding-based bypasses (encoding), toxicity, and more. garak is described in the paper "garak: A Framework for Security Probing Large Language Models" (arXiv:2406.11036) and is distributed from the NVIDIA/garak GitHub repository.
The scanner is generator-agnostic. It can target Hugging Face models loaded locally, OpenAI-compatible APIs, AWS Bedrock, Replicate, Cohere, NIM endpoints, GGUF/llama.cpp models, and arbitrary REST endpoints via a JSON generator spec. After a run, garak emits a .report.jsonl line-delimited log of every attempt and detector verdict, a human-readable .report.html, a garak.log debug log, and a hit log of confirmed vulnerabilities. The terminal output prints a per-probe, per-detector pass/fail summary with a hit rate (for example dan.Dan_11_0 jailbreak: FAIL ok on 38/40), which is the primary artifact you interpret.
This skill maps to the MITRE ATLAS techniques AML.T0051 (LLM Prompt Injection) and AML.T0054 (LLM Jailbreak) because garak operationalizes both: it crafts prompt-injection and jailbreak inputs at scale and measures whether the target's guardrails hold. It supports the NIST AI RMF MEASURE-2.7 subcategory by providing repeatable, quantitative security/resilience measurement of a deployed AI system.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 222 lines · 49 tokens per session scan B f0e3aec650e4
red-teaming-llms-with-garak is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 49 tokens to every session and 2,830 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (nullifies safety policies). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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ai-hacker
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ai-jailbreak-prompt-injection
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cost-aware-llm-pipeline
A planning guide for choosing language models and managing the amount of conversation context used by an AI coding workflow. It groups tasks by complexity and gives rules for avoiding context overflow during long sessions.