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BB prompt-injection-tester

Test LLM applications for prompt injection vulnerabilities — run attack simulations, evaluate defenses, and generate hardening recommendations for AI systems.

ClawHub Agent Skills author: charlie-morrison v1.0.1 MIT-0 2 files body ≈ 1 517 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 71/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

AnalyzerAI and agentstype 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%
97
Quality 40%
75
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
50
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Risky intent intent-offensive-security SKILL.md:16
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    "Red team the AI assistant's system prompt defenses"
    detector
  • low Risky intent intent-offensive-security SKILL.md:58
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - Privilege escalation: Making the model perform unauthorized actions

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 2. 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 71/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 37 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1517 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 158: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 37 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is an instruction-only skill for authorized LLM prompt-injection testing; the risky-looking phrases are disclosed test examples, not hidden commands.
LLM: benign (high) · VirusTotal: · 29 May 2026