probegpt 0.3.1

Automated red-team discovery for AI models

probegpt

Automated red-team discovery for AI models. Generates adversarial probe prompts across 8 attack strategies, sends them to a target model, judges responses against your objective, and iteratively refines the best-scoring probes using intelligent seed grading.

Install

pipx install probegpt

Usage

probegpt

Full-screen TUI. Set up your models in Config, then go to Run Discovery, enter your objective, and hit Run.

Models

Any of the following providers can be assigned independently to the target, generator, and judge roles:

Provider	Auth
Azure OpenAI	az login
OpenRouter	OPENROUTER_API_KEY env var
GCP Vertex AI	gcloud auth application-default login
AWS Bedrock	aws configure
Cerebras	CEREBRAS_API_KEY env var
Mistral	MISTRAL_API_KEY env var

Config is saved to ~/.outofthebox/config.json.

How it works

1. Loads 9 seed probes covering known red-team techniques
2. Each iteration distributes candidates across all enabled attack strategies (weighted)
3. Every probe is sent to the target model; the judge scores the response 0.0 → 1.0
4. Probes scoring ≥ 0.5 are sent to the seed grader, which analyzes why the probe worked and extracts technique tags
5. Novel, high-transferability probes are added to a weighted seed pool; the generator is told which techniques are working
6. Results optionally exported to JSON

Attack strategies

Eight strategies run in parallel each iteration. Candidates are distributed proportionally to their configured weights.

Strategy	Technique family
objective	Fresh probes written directly for the objective
mutation	Variations of high-scoring seeds from the pool
encoding	base64, ROT13, leetspeak, fragmentation, reversal — half LLM-generated, half programmatic
roleplay	Evil-twin personas, simulation frames, nested hierarchy, game context
persuasion	Emotional appeal, urgency, flattery, logical fallacy, social proof
linguistic	Typos, phonetic spelling, double negatives, logic tricks, passive voice
structural	Payload splitting, chain-of-thought hijack, prefix/suffix injection, context stuffing
distraction	Benign prefix, topic pivot, attention splitting, distract-and-attack

Enable/disable strategies and adjust their relative weights in Config → Strategies.

Intelligent seed grading

After each successful probe (score ≥ 0.5), an LLM agent analyzes it and assigns:

• technique tags — what made it work (57-tag vocabulary)
• novelty — how different it is from existing seeds (duplicates are discarded)
• transferability — how likely the technique generalises to other objectives

From iteration 2 onward, the generator receives the top working technique tags as a feedback hint, and mutation sampling weights toward high-scoring, novel, transferable seeds.

Requirements

• Python 3.11+
• At least one configured provider for each role (target, generator, judge)