AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsnpx agentmods add skills/sickn33/agentic-awesome-skills/ai-agent-securityWrote 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/sickn33/agentic-awesome-skills/ai-agent-security)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-agent-security"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-agent-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/sickn33/agentic-awesome-skills/ai-agent-security"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-agent-security.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 | $0.00025 | $0.03587 |
| Opus 5.5 | $0.00010 | $0.01435 |
| Sonnet 5.5 | $0.00005 | $0.00717 |
| Haiku 4.5 | $0.00003 | $0.00359 |
Grade B, and why
ai-agent-security scanned grade B with 3 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 yesterday.
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.
Harvests environment variablesmediumData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
**Privilege Escalation** — The agent chains low-privilege tool calls to achieve high-privilege outcomes (e.g., read config -> extract credentials -> call admin API). Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Asks for rootlowPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
curl -fsSL https://gvisor.dev/archive.key | sudo gpg --dearmor -o /usr/share/keyrings/gvisor-archive-keyring.gpg Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
(r"(?:curl|wget|nc|ncat)\s+", "network_command_inject"), Copies of this mod
2 near-identical copies found in the catalogue:
- ai-agent-security — 100% identical, 39 lines differ
- ai-agent-security — 100% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Security
Protect agentic AI systems from adversarial input, unsafe tool execution, data leakage, and privilege abuse with layered security controls.
These are partial design sketches, not a working security boundary. Regexes/classifiers have false positives and false negatives; prompt text cannot grant permissions. Enforce tenant identity, resource scope and approved actions in the tool service using authenticated server-side context. Input signing authenticates origin, not the safety of signed content. Bind approvals to the exact operation/arguments and recheck immediately before execution. Test failure paths and concurrency before production use.
Prerequisites
- Python 3.10+ for guardrail code examples
- Docker or Podman for sandbox execution
- OpenTelemetry collector for audit logging
- Familiarity with your agent framework (LangChain, CrewAI, Autogen, custom)
- Access to policy engine (OPA/Cedar) for permission boundaries
Threat Model — STRIDE for AI Agents
AI agents introduce a unique threat surface. Apply STRIDE specifically to agentic components:
| Threat | Agent-Specific Example | Control |
|---|---|---|
| Spoofing | Attacker crafts input that mimics a trusted internal tool response | Signed tool responses, HMAC verification |
| Tampering | Prompt injection modifies agent reasoning mid-chain | Input validation, prompt armoring |
| Repudiation | Agent takes destructive action with no audit trail | Immutable structured logging |
| Information Disclosure | Agent leaks PII, secrets, or internal architecture in responses | Output filtering, content classifiers |
| Denial of Service | Adversarial prompt causes infinite tool loops or token exhaustion | Rate limits, token budgets, circuit breakers |
| Elevation of Privilege | Agent escalates from read-only to write via chained tool calls | RBAC per tool, least-privilege scoping |
Key Threat Categories
Prompt Injection — Untrusted content (user input, web scrapes, document contents) manipulates the agent's system prompt or reasoning chain to execute unintended actions.
What ships with it
1 file 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.
- yesterday Changed · +1 lines e868427d541d
- 1mo ago First seen · 394 lines · 25 tokens per session scan B 66776bd8ae18
ai-agent-security is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,503 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 3,587 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 3 findings (harvests environment variables, asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-21.
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