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 BagelHole/DevOps-Security-Agent-Skills --skill llm-app-securitygit clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-SkillsWrote 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/bagelhole/devops-security-agent-skills/llm-app-security)<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/llm-app-security"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/llm-app-security/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/bagelhole/devops-security-agent-skills/llm-app-security"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/llm-app-security.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 Prompt Injection · line 89 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 Memory Poisoning · line 910 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium System Prompt Leakage · line 104 Skill contains patterns that could indirectly extract system prompts through rephrasing, translation, summarization, or side-channel techniques.Fix: Guard against indirect extraction by refusing to summarize, translate, or rephrase system instructions. Add explicit anti-extraction clauses.
- medium Data Exfiltration · line 151 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00024 | $0.08130 |
| Opus 5 | $0.00012 | $0.04065 |
| Sonnet 5 | $0.00005 | $0.01626 |
| Haiku 4.5 | $0.00002 | $0.00813 |
Grade B, and why
llm-app-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 6d 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.
# Patterns that signal an attempt to 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 — 1,018 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Application Security
Harden chatbots, RAG pipelines, and AI features embedded in SaaS products against prompt injection, data leakage, abuse, and compliance violations.
When to Use
Apply this skill whenever you are building or operating:
- Customer-facing chatbots -- support bots, sales assistants, or any conversational UI backed by an LLM.
- RAG-augmented applications -- internal knowledge bases, document Q&A, or code assistants that retrieve context from a vector store before generating a response.
- AI features inside SaaS products -- summarization, auto-complete, content generation, or classification endpoints exposed to end users.
- Internal copilots -- developer tools, HR bots, or finance assistants that handle sensitive corporate data.
- Multi-tenant platforms -- any system where multiple customers share the same LLM infrastructure.
If your application sends user-controlled text to an LLM and returns the result, every section below applies.
OWASP LLM Top 10 -- Risk Map and Mitigations
The OWASP Top 10 for LLM Applications (2025) defines the most critical risks. The table below maps each risk to concrete controls implemented later in this document.
| # | Risk | Key Mitigation | Section |
|---|---|---|---|
| LLM01 | Prompt Injection | Input validation, instruction hierarchy | Input Validation, System Prompt Protection |
| LLM02 | Insecure Output Handling | Output sanitization, PII scrubbing | Output Safety |
| LLM03 | Training Data Poisoning | Document ingestion scanning | Secure RAG Pipeline |
| LLM04 | Model Denial of Service | Per-user token budgets, rate limiting | Rate Limiting |
| LLM05 | Supply Chain Vulnerabilities | Pin model versions, verify checksums | Compliance |
| LLM06 | Sensitive Information Disclosure | PII detection, tenant isolation | Output Safety, Tenant Isolation |
| LLM07 | Insecure Plugin Design | Tool allowlists, parameter validation | System Prompt Protection |
| LLM08 | Excessive Agency | Least-privilege tool scopes | System Prompt Protection |
| LLM09 | Overreliance | Provenance tracking, confidence scores | Secure RAG Pipeline |
| LLM10 | Model Theft | Access controls, API key rotation | Rate Limiting, Compliance |
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.
- 6d ago First seen · 1,018 lines · 24 tokens per session scan B 5b48ec511061
llm-app-security is a skill published in the GitHub repository BagelHole/DevOps-Security-Agent-Skills (1,071 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 8,130 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
bedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
aws-bedrock-ai
WORKFLOW SKILL — Amazon Bedrock and AWS AI design: foundation model selection, knowledge bases (RAG), agents for bedrock, guardrails, provisioned throughput, batch inference, fine-tuning, KMS, VPC endpoints, regional GA, and per-provider licensing.
jetson-inference-mem-tune
Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.
neuron-test-engineer
Write tests for Neuron AI agents, RAG systems, workflows, and tools using the built-in testing utilities. Use this skill when the user mentions testing agents, writing unit tests, mocking AI providers, testing tool execution, verifying RAG retrieval, testing workflow behavior, or creating test cases for Neuron AI…
neuron-rag-specialist
Implement RAG (Retrieval-Augmented Generation) with Neuron AI including vector stores, embeddings providers, document loaders, and retrieval strategies. Use this skill whenever the user mentions RAG, retrieval, vector search, document retrieval, semantic search, knowledge bases, chat with documents, or wants to build…
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.