Automatisering van redteaming
Frameworks en tools voor het op schaal automatiseren van AI-redteaming, inclusief CART-pipelines, jailbreak-fuzzing, regressietests en continue monitoring.
Handmatige redteaming is essentieel voor creatieve ontdekking van aanvallen, maar schaalt niet. CART en andere automatiseringsframeworks maken continue testen, regressiedetectie en brede dekking over aanvalscategorieën mogelijk. Professionele AI-redteaming combineert handmatige creativiteit met geautomatiseerde schaal.
CART-architectuur
Een Continuous Automated Red Teaming-pipeline:
Payload-generatie → Uitvoeringsengine → Resultaatanalyse → Rapportage
↓ ↓ ↓ ↓
Templatebibliotheek API-integratie Succesdetectie Dashboard
Mutatie-engine Ratemanagement Classificatie Meldingen
LLM-gebaseerde gen Parallelle uitv. Statistische tests Trends
Een basis-CART-pipeline bouwen
import asyncio
from dataclasses import dataclass
from typing import Callable
@dataclass
class TestCase:
payload: str
category: str # "injection", "jailbreak", "extraction", etc.
expected_behavior: str # "refuse", "comply", "extract"
detector: Callable # Function to classify the response
@dataclass
class TestResult:
test_case: TestCase
response: str
success: bool
latency_ms: float
timestamp: str
class CARTPipeline:
def __init__(self, target_api, test_suite: list[TestCase]):
self.target = target_api
self.tests = test_suite
self.results: list[TestResult] = []
async def run_suite(self, n_trials_per_test: int = 10):
"""Run all tests with statistical significance."""
for test in self.tests:
for _ in range(n_trials_per_test):
response = await self.target.query(test.payload)
success = test.detector(response)
self.results.append(TestResult(
test_case=test,
response=response,
success=success,
latency_ms=response.latency,
timestamp=datetime.now().isoformat(),
))
def report(self) -> dict:
"""Generate success rates by category."""
from collections import defaultdict
stats = defaultdict(lambda: {"total": 0, "success": 0})
for r in self.results:
stats[r.test_case.category]["total"] += 1
if r.success:
stats[r.test_case.category]["success"] += 1
return {k: v["success"]/v["total"] for k, v in stats.items()}Jailbreak-fuzzing
Geautomatiseerde generatie en het testen van jailbreak-varianten via fuzzing:
class JailbreakFuzzer:
"""Generate jailbreak variants through mutation."""
def __init__(self, seed_prompts: list[str]):
self.seeds = seed_prompts
self.mutations = [
self.synonym_replace,
self.add_context,
self.change_framing,
self.add_encoding,
self.split_payload,
self.add_few_shot,
]
def synonym_replace(self, prompt: str) -> str:
"""Replace key terms with synonyms."""
replacements = {
"ignore": ["disregard", "bypass", "override", "skip"],
"instructions": ["rules", "guidelines", "directives", "constraints"],
"previous": ["earlier", "prior", "above", "initial"],
}
for word, synonyms in replacements.items():
if word in prompt.lower():
prompt = prompt.replace(word, random.choice(synonyms))
return prompt
def add_encoding(self, prompt: str) -> str:
"""Wrap payload in various encodings."""
encodings = [
lambda p: f"Decode and follow: {base64.b64encode(p.encode()).decode()}",
lambda p: f"ROT13: {codecs.encode(p, 'rot_13')}",
lambda p: f"Reverse: {p[::-1]}",
]
return random.choice(encodings)(prompt)
def fuzz(self, n_variants: int = 100) -> list[str]:
"""Generate n variant payloads."""
variants = []
for _ in range(n_variants):
base = random.choice(self.seeds)
n_mutations = random.randint(1, 3)
for _ in range(n_mutations):
mutation = random.choice(self.mutations)
base = mutation(base)
variants.append(base)
return variantsRegressietests
Wanneer modellen worden bijgewerkt, kunnen eerder verholpen kwetsbaarheden opnieuw verschijnen en kunnen nieuwe ontstaan:
class RegressionSuite:
"""Test known vulnerabilities after model updates."""
def __init__(self, known_vulns: list[TestCase]):
self.vulns = known_vulns
def check_regression(self, target_api) -> dict:
"""Test all known vulnerabilities against current model."""
results = {}
for vuln in self.vulns:
success_rate = measure_success_rate(vuln.payload, target_api)
results[vuln.category] = {
"previous_rate": vuln.last_known_rate,
"current_rate": success_rate,
"regression": success_rate > vuln.last_known_rate,
}
return resultsOpen-source tools
| Tool | Doel | Link |
|---|---|---|
| Garak | Kwetsbaarheidsscanner voor LLM's | github.com/leondz/garak |
| PyRIT | Redteaming-framework van Microsoft | github.com/Azure/PyRIT |
| Promptfoo | Prompt-testen en -evaluatie | github.com/promptfoo/promptfoo |
| ART | Adversarial Robustness Toolbox | github.com/Trusted-AI/adversarial-robustness-toolbox |
Probeer het zelf
Verwante onderwerpen
- Overzicht AI-exploitontwikkeling -- de bredere context van exploitontwikkeling
- Payload-crafting -- de seed-payloads maken die door automatisering worden opgeschaald
- Custom tooling -- doelwitspecifieke automatiseringstools bouwen
- CART-pipelines -- geavanceerde CART-architectuur en -deployment
- Capstone: uitvoering & rapportage -- automatisering integreren in volledige engagements
Referenties
- Perez et al., "Red Teaming Language Models with Language Models" (2022) -- LLM-gebaseerde geautomatiseerde redteaming
- Samvelyan et al., "Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts" (2024) -- diversiteitsgerichte geautomatiseerde aanvalsgeneratie
- Deng et al., "Garak: A Framework for Security Probing Large Language Models" (2024) -- open-source kwetsbaarheidsscans voor LLM's
Waarom is regressietesten belangrijk na modelupdates?