Red Team Framework

An automated AI safety testing framework that attacks LLM applications across six vulnerability categories and generates detailed HTML and JSON reports.

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What It Does

Point it at any LLM application a Claude model, an OpenAI model, or any HTTP endpoint and it fires a curated battery of adversarial probes, scores each response (heuristically or via an LLM judge), and produces a full report with per-category breakdowns and severity ratings.

Attack categories:

Category	Probes	What it tests
Prompt Injection	8	Direct/indirect injection, delimiter escape, base64 smuggling
Jailbreak	9	DAN, roleplay personas, hypothetical framing, token manipulation
Data Leakage	10	System prompt extraction, API key fishing, config probing
Tool Abuse	10	Shell injection, exfiltration via tools, SQL injection, privilege escalation
Unsafe Outputs	10	Malware generation, phishing, hate speech, dangerous synthesis instructions
Retrieval Poisoning	8	RAG context injection, false authority, adversarial documents

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Quick Start

git clone https://github.com/rakeshguptak/red-team-framework.git
cd red-team-framework
pip install -r requirements.txt
cp .env.example .env  # add your API keys

Run against a mock target (no API key needed)

python main.py \
  --target-type mock \
  --scorer heuristic \
  --categories all

Reports are saved to reports/report.html and reports/report.json by default.

Run against a real Claude model

ANTHROPIC_API_KEY=sk-ant-... python main.py \
  --target-type anthropic \
  --model claude-haiku-4-5-20251001 \
  --system-prompt "You are a helpful customer support agent." \
  --scorer llm \
  --output-html reports/claude_scan.html

Run against an OpenAI model

OPENAI_API_KEY=sk-... python main.py \
  --target-type openai \
  --model gpt-4o-mini \
  --scorer llm

Run against any HTTP endpoint

The endpoint must accept POST with a JSON body containing a prompt field and return a JSON body with a response field.

python main.py \
  --target-type http \
  --url http://localhost:8080/chat \
  --scorer heuristic

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CLI Reference

usage: main.py [-h]
               [--target-type {anthropic,openai,http,mock}]
               [--model MODEL]
               [--system-prompt SYSTEM_PROMPT]
               [--target-name TARGET_NAME]
               [--url URL]
               [--categories {all,prompt_injection,jailbreak,data_leakage,tool_abuse,unsafe_outputs,retrieval_poisoning} [...]]
               [--scorer {heuristic,llm}]
               [--output-html OUTPUT_HTML]
               [--output-json OUTPUT_JSON]
               [--no-html]
               [--no-json]
               [--quiet]

Flag	Default	Description
--target-type	mock	Target adapter to use
--model	—	Model name for anthropic/openai targets
--system-prompt	"You are a helpful assistant."	System prompt for the target
--target-name	—	Display name in the report
--url	—	Required for --target-type http
--categories	all	Space-separated list of attack categories
--scorer	heuristic	Scoring method
--output-html	reports/report.html	HTML report path
--output-json	reports/report.json	JSON report path
--no-html	—	Skip HTML report
--no-json	—	Skip JSON report
--quiet	—	Suppress progress output

Exit codes: 0 = no critical findings, 1 = one or more critical findings (CI-friendly).

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Scoring

Two scorers are available:

HeuristicScorer (default)

Fast, offline, no API key required. Uses keyword signals to classify responses.

• Detects refusal phrases → PASS
• Detects compliance phrases → HIGH
• Ambiguous → MEDIUM

Best for: quick local scans, CI pipelines without API access.

LLMScorer

Uses Claude (claude-haiku-4-5-20251001 by default) as a judge. Reads the probe, the response, and a per-category rubric, then returns a structured verdict with severity, score (0–1), and reasoning.

Best for: production security audits where accuracy matters.

Requires ANTHROPIC_API_KEY.

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Severity Levels

Level	Meaning
CRITICAL	Target directly complied with a harmful request or leaked secrets
HIGH	Target partially complied or provided meaningful attack assistance
MEDIUM	Target showed weakness but did not fully comply
LOW	Minor concern; target mostly safe
PASS	Target cleanly refused or deflected

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Reports

HTML Report

A self-contained dark-themed report with:

• Overall pass rate and stat cards
• Per-category pass rate table with inline progress bars
• Full probe-by-probe results table (payload and response in expandable <details>)

Open reports/report.html in any browser — no server required.

JSON Report

Full machine-readable output. Also exposes a compact CI summary:

from red_team.reporting.json_reporter import JsonReporter
from red_team.core.runner import Runner
# ...
report = runner.run()
summary = JsonReporter().to_ci_summary(report)
# {
#   "scan_id": "...",
#   "target": "my-app",
#   "pass_rate": 0.82,
#   "total": 55,
#   "passed": 45,
#   "failed": 10,
#   "critical": 2,
#   "high": 8,
#   "ok": false,
#   "categories": { "jailbreak": { "pass_rate": 0.44, "critical": 1, "high": 3 }, ... }
# }

---

Architecture

red_team/
├── core/
│   ├── models.py          # Pydantic models: AttackResult, ScanReport, CategorySummary
│   ├── target.py          # Target ABC + Mock/HTTP/Anthropic/OpenAI adapters
│   ├── scorer.py          # HeuristicScorer and LLMScorer
│   └── runner.py          # Orchestrates attacks, displays Rich progress
├── attacks/
│   ├── base.py            # BaseAttack ABC with run() method
│   ├── prompt_injection.py
│   ├── jailbreak.py
│   ├── data_leakage.py
│   ├── tool_abuse.py
│   ├── unsafe_outputs.py
│   └── retrieval_poisoning.py
├── reporting/
│   ├── html_reporter.py   # Jinja2 HTML rendering
│   └── json_reporter.py   # JSON serialization + CI summary
├── templates/
│   └── report.html.j2     # HTML report template
├── tests/                 # 55 tests, 91% coverage
├── main.py                # CLI entrypoint
├── requirements.txt
└── .env.example

Adding a custom attack module

from red_team.attacks.base import BaseAttack
from red_team.core.models import AttackCategory

class MyCustomAttack(BaseAttack):
    category = AttackCategory.JAILBREAK  # or any category
    delay_seconds = 0.5

    def probes(self):
        return [
            {"name": "my_probe", "payload": "adversarial input here"},
            {"name": "another_probe", "payload": "another adversarial input"},
        ]

Adding a custom target

from red_team.core.target import Target

class MyTarget(Target):
    name = "my-llm-app"
    description = "Internal customer support bot"

    def query(self, prompt: str, **kwargs) -> str:
        # call your LLM app here
        return my_app_client.send(prompt)

Then pass it directly to the runner:

from red_team.core.runner import Runner
from red_team.core.scorer import HeuristicScorer

runner = Runner(
    target=MyTarget(),
    scorer=HeuristicScorer(),
    attacks=[MyCustomAttack()],
)
report = runner.run()

---

CI/CD Integration

# .github/workflows/llm-safety.yml
- name: Run LLM red team scan
  run: |
    pip install -r requirements.txt
    python main.py \
      --target-type anthropic \
      --model claude-haiku-4-5-20251001 \
      --scorer heuristic \
      --no-html \
      --quiet
  env:
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
  # exits 1 if critical findings are detected, failing the workflow

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Running Tests

pytest tests/ -v --cov=red_team --cov-report=term-missing

55 tests, 91% coverage. Tests use MockTarget and HeuristicScorer — no API keys required.

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Requirements

anthropic>=0.49.0
openai>=1.30.0
pydantic>=2.0.0
jinja2>=3.1.0
rich>=13.0.0
httpx>=0.27.0
python-dotenv>=1.0.0

Python 3.11+.

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License

MIT