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BC ai-vulnerability-tracker

AI 漏洞追踪器 - 在 GitHub 和微信公众号搜索近一个月的 AI 相关漏洞（提示词注入、提示词越狱等），并推送到飞书表格。支持去重和翻译。 搜索关键字: prompt injection, prompt jailbreak, LLM vulnerability, AI security, adversarial prompt, jailbreak attack 数据源: - GitHub: 最近一个月的安全漏洞提交 - 微信公众号: AI 安全相关文章 使用方式: - 运行技能执行一次搜索和推送 - 配置 cron 进行定时执行

ClawHub Agent Skills author: Octday v0.1.0 MIT-0 5 files body ≈ 262 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceGitHubAI and agentsSecuritytype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
99
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token index.js:17
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    appSecret: process.env.FEISHU_APP_SECRET || 'aaMN…j5u',
    quoted

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 262 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 274: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This tracker does what it broadly claims, but it ships with embedded Feishu credentials and writes to a hard-coded Feishu destination that users may not control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026