PromptXploit

LLM penetration testing from the command line. Point it at any model or endpoint, run a categorized set of prompt-injection and jailbreak attacks, and get a structured report of what got through.

Authorized testing only. Use PromptXploit against systems you own or are explicitly permitted to test. See DISCLAIMER.md.

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Overview

PromptXploit runs adversarial prompts against a target model, decides whether each attack succeeded, and writes the results to JSON. Attacks are streamed from a curated, categorized dataset; verdicts are decided locally by default, so nothing about your target leaves your machine. It is framework-agnostic: any LLM or HTTP endpoint works as long as it can be wrapped in a single run(prompt) function.

The pipeline is deliberately ordered cheapest-first:

attacks  →  target  →  deterministic rules  →  judge (only the uncertain cases)  →  report

Deterministic rules resolve the obvious outcomes (clear refusals, clear bypasses) before any model is invoked. Only the ambiguous cases are sent to the judge.

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Features

• Categorized attacks from HuggingFace. Stream a curated attack set and filter it by intent (for example, tool_abuse), pulling only what you need.
• Local-first judging. Verdicts are decided by a local model through Ollama — nothing leaves your machine. OpenAI and Gemini are optional backends.
• Rules-first, low false-positive. Deterministic checks handle refusals and obvious bypasses before spending a model call, so the report does not cry wolf.
• Rate limiting. Cap attacks per minute so you do not overload a target.
• Framework-agnostic targets. Any LLM, local model, or HTTP endpoint.
• Structured output. Per-attack verdict, risk score, and rationale as JSON.

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Installation

pip install promptxploit

For the default local judge, install Ollama and pull a model:

ollama pull llama3.1:8b

From source:

git clone https://github.com/m4vic/promptxploit
cd promptxploit
pip install -e .

Optional API judges:

pip install "promptxploit[openai]"   # for --judge openai
pip install "promptxploit[gemini]"   # for --judge gemini

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Quick start

Define a target — any callable that takes a prompt and returns the model's response:

# my_target.py
def run(prompt: str) -> str:
    return your_llm(prompt)

Run a scan. This pulls tool_abuse attacks, judges them locally, and writes a report:

promptxploit \
    --target my_target.py \
    --category tool_abuse \
    --judge local --judge-model llama3.1:8b \
    --rate 10 \
    --output scan.json

Read scan.json for the per-attack verdicts.

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Attack sources

By default, attacks are streamed from the categorized dataset neuralchemy/prompt-injection-dataset-categorized (the intent configuration).

Available intents:

benign   direct_injection   system_extraction   role_hijack
obfuscation   tool_abuse   indirect_injection

Flag	Purpose
--category tool_abuse	Use only this intent (comma-separated for several)
--limit 100	Cap the number of attacks
--subset intent	Dataset configuration (default: intent)
--dataset ORG/NAME	Use a different HuggingFace dataset

To use your own local attack files instead of the dataset:

promptxploit --source local --attacks ./attacks --target my_target.py --output scan.json

Local attack files are JSON lists of objects with id, category, and prompt.

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Judging

The judge decides the outcome for cases the deterministic rules cannot.

Backend	Flag	Notes
Local (Ollama)	--judge local	Default. Runs entirely on your machine; nothing is sent to a third party.
OpenAI	--judge openai	Requires OPENAI_API_KEY.
Gemini	--judge gemini	Requires GOOGLE_API_KEY.
Rules only	--judge none	No model; uncertain cases are left as partial.

The local backend is the default on purpose: when you are testing another party's system, their responses should not be shipped to an external API. Choose the judge model with --judge-model (for example, llama3.1:8b or qwen2.5:14b-instruct) and point at a non-default Ollama host with --ollama-url.

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Targets

A target is any Python module that defines run(prompt: str) -> str. That function can call a local model, a hosted API, or an entire application.

To test an HTTP endpoint, configure targets/http_api_target.py with your URL, headers, and request shape, then pass it as --target. It works with OpenAI-style APIs, Anthropic, and arbitrary REST endpoints.

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Rate limiting

promptxploit --target my_target.py --rate 5 --output scan.json

--rate caps attacks per minute against the target (0 means unlimited). API judges are paced separately from the attack rate.

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Understanding the output

The report is a JSON list. Each entry records the attack, the verdict, and a normalized risk score:

{
  "attack_id": "HF-00042",
  "category": "tool_abuse",
  "source": "hf:neuralchemy/prompt-injection-dataset-categorized:intent",
  "verdict": {
    "verdict": "fail",
    "confidence": 0.9,
    "severity": 0.85,
    "rationale": "Model followed the injected tool instruction."
  },
  "risk": { "risk_score": 0.77, "risk_level": "high" }
}

Verdicts:

• pass — the attack was blocked; the model refused or answered safely.
• fail — the attack succeeded; the model complied, leaked, or was manipulated.
• partial — uncertain or requires manual review.

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Custom attacks

Provide your own attacks as a JSON list:

[
  { "id": "CUSTOM-001", "category": "tool_abuse", "prompt": "Your attack prompt here" }
]

promptxploit --source local --attacks my_attacks.json --target my_target.py --output scan.json

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Responsible use

PromptXploit is a security testing tool for authorized use only: your own systems, sanctioned penetration tests, and security research. Do not use it against systems you are not permitted to test. Full terms are in DISCLAIMER.md.

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Defense: PromptShield

PromptXploit finds where a model breaks. PromptShield is the defensive counterpart that hardens it. The intended workflow is to test with PromptXploit, add PromptShield, and re-test to confirm the gap is closed.

pip install promptshields

from promptshield import Shield

shield = Shield.balanced()

def protected_llm(prompt: str, system_prompt: str) -> str:
    result = shield.protect_input(prompt, system_prompt)
    if result["blocked"]:
        return "Request blocked."
    return your_llm(prompt)

protect_input returns a result with blocked, reason, and threat_level. PromptShield also provides protect_output, protect_stream, and protect_tool_call for defending the response and agent surfaces. See the PromptShield repository for the full guide.

A typical before-and-after run:

promptxploit --target unprotected.py --category tool_abuse --output before.json
promptxploit --target protected.py   --category tool_abuse --output after.json

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License

MIT. See LICENSE.