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 RedHatProductSecurity/prodsec-skills --skill ai-systems-securitygit clone --depth 1 https://github.com/RedHatProductSecurity/prodsec-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/redhatproductsecurity/prodsec-skills/ai-systems-security)<a href="https://agentmods.dev/skills/redhatproductsecurity/prodsec-skills/ai-systems-security"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/prodsec-skills/ai-systems-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/redhatproductsecurity/prodsec-skills/ai-systems-security"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/prodsec-skills/ai-systems-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 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 Tool Misuse · line 214 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Data Exfiltration · line 123 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.
- medium Data Exfiltration · line 130 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.
- medium Agent Snooping · line 257 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 258 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 259 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- low Privilege Escalation · line 186 Skill requests more permissions than appear necessary for its stated functionality. Review if elevated access is justified.Fix: Request only the minimum permissions required. Document why each permission is needed. Remove broad permissions like '*' or 'all'.
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 | $0.00041 | $0.02165 |
| Opus 5 | $0.00020 | $0.01082 |
| Sonnet 5 | $0.00008 | $0.00433 |
| Haiku 4.5 | $0.00004 | $0.00216 |
Grade A, and why
ai-systems-security 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
requests.post(tool_url, headers=headers, json=params) Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Never use `os.system()` or `shell=True`** — always use parameterized APIs How it starts
The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Systems Security
Security guidance for teams that serve, integrate, or operate LLMs and AI agents — not for teams training or fine-tuning models. Covers the attack surface introduced when products call LLM APIs, run agent workflows, or expose tool servers.
When to Use
- Integrating an LLM API (OpenAI, Anthropic, self-hosted) into a product
- Deploying model-serving infrastructure (OpenShift AI, RHOAI, vLLM)
- Building or reviewing agent workflows that take actions on behalf of users
- Connecting MCP servers to agents or LLM-powered features
- Reviewing AI system architecture for security gaps
When NOT to Use
- Reviewing AI-generated code for correctness (use
ai-code-review) - General web application security without AI components (use
web-application-security) - Container hardening without AI-specific concerns (use
container-hardening)
1. Secure LLM Integration
Third-party model providers
- Obtain information security approval for each provider before integration
- All connections MUST use TLS — never send prompts or receive responses over unencrypted channels
- Never hardcode API keys in source code, commit them to version control, or bake them into container images
- Store credentials in secret management systems (Vault, Kubernetes Secrets) or inject via environment variables at runtime
- Rotate API credentials on a regular schedule and monitor usage for unexpected patterns
Credential storage:
FORBIDDEN: Hardcoded in source, committed to git, baked in images
REQUIRED: Secret management (Vault, K8s Secrets) or env vars injected at runtime
Self-hosted models
- Scan models for malicious code before loading — avoid unsafe serialization formats (pickle) that execute code on deserialization; prefer SafeTensors
- Verify model provenance and integrity (signatures, checksums) before deployment
- Isolate model-serving processes with dedicated service accounts and restricted network access
2. Prompt Injection Defense
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.
- 10d ago First seen · 260 lines · 41 tokens per session scan A 457245cec112
ai-systems-security is a skill published in the GitHub repository RedHatProductSecurity/prodsec-skills (52 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 2,165 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…