AI-Infra-Guard is an AI security red-teaming platform that scans agents, skills, MCP servers, and AI infrastructure and evaluates LLM jailbreak resistance. It is used to identify security risks and vulnerabilities in AI systems. Catalogue add-ons support its scanning and evaluation workflows.
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 Tencent/AI-Infra-Guard --skill owasp-asigit clone --depth 1 https://github.com/Tencent/AI-Infra-GuardWrote 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/tencent/ai-infra-guard/owasp-asi)<a href="https://agentmods.dev/skills/tencent/ai-infra-guard/owasp-asi"><img src="https://agentmods.dev/badge/skills/tencent/ai-infra-guard/owasp-asi/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/tencent/ai-infra-guard/owasp-asi"><img src="https://agentmods.dev/badge/skills/tencent/ai-infra-guard/owasp-asi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00872 |
| Opus 5 | $0.00016 | $0.00436 |
| Sonnet 5 | $0.00007 | $0.00174 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
owasp-asi scanned grade A with 0 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 12d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OWASP ASI Classification Framework
OWASP Top 10 for Agentic Applications 2026 - Standardized risk classification for AI agent security.
Risk Categories
| ID | Risk Type | Key Indicators |
|---|---|---|
| ASI01 | Agent Goal Hijack | Prompt injection, instruction override, goal manipulation |
| ASI02 | Tool Misuse & Exploitation | Unauthorized tool calls, parameter tampering, unvalidated inputs |
| ASI03 | Identity & Privilege Abuse | Auth bypass, permission escalation, missing authorization |
| ASI04 | Agentic Supply Chain | Malicious dependencies, compromised tools, package poisoning |
| ASI05 | Unexpected Code Execution | RCE, command injection, code evaluation |
| ASI06 | Memory & Context Poisoning | Data leakage, context manipulation, memory corruption |
| ASI07 | Insecure Inter-Agent Comm | Unencrypted channels, data exposure between agents |
| ASI08 | Cascading Failures | Error propagation, chain reaction vulnerabilities |
| ASI09 | Human-Agent Trust Exploit | Social engineering, deceptive responses |
| ASI10 | Rogue Agents | Malicious agent behavior, unauthorized actions |
Detection Source → ASI Mapping
| Detection Source | Type | Primary ASI | Secondary ASI |
|---|---|---|---|
data-leakage-detection |
Skill | ASI06, ASI07 | ASI01, ASI03 |
tool-abuse-detection |
Skill | ASI02, ASI05, ASI07 | ASI03 |
indirect-injection-detection |
Skill | ASI01 | ASI06 |
authorization-bypass-detection |
Skill | ASI03 | ASI09 |
agentic-supply-chain-detection |
Skill | ASI04 | ASI10 |
unexpected-code-execution-detection |
Skill | ASI05 | ASI04 |
inter-agent-comm-security-detection |
Skill | ASI07 | ASI06 |
cascading-failure-detection |
Skill | ASI08 | ASI10 |
human-agent-trust-exploit-detection |
Skill | ASI09 | ASI01 |
| Prompt Injection tests | Dialogue | ASI01, ASI06 | ASI09 |
| Code Audit | Agent | ASI04, ASI05 | ASI10 |
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.
- 12d ago First seen · 95 lines · 33 tokens per session scan A 150b8d5af142
owasp-asi is a skill published in the GitHub repository Tencent/AI-Infra-Guard (6,221 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 872 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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