ADAPTIVE ADVERSARIAL ML

Static rules.
Adaptive attacks.

Autonomous red team agents that learn, adapt, and bypass defenses. When your attack gets blocked, ADCL pivots. Your current tools don't.

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$ adcl scan --target llm-api
[*] Initializing agent pool...
[*] Testing prompt injection vectors
[!] Attack blocked by input filter
[*] Adapting payload encoding...
[*] Retrying with unicode obfuscation
[✓] Jailbreak successful
[✓] Model extraction possible
 
$ _
01

Multi-Agent Attack Chains

Specialized agent pools for prompt injection, model extraction, data poisoning. Coordinated campaigns, not isolated probes.

02

Adaptive Bypass

AI that actually learns when blocked. Mutates payloads, switches techniques, finds the path your WAF missed.

03

OWASP LLM Top 10

Full coverage of LLM01-LLM10. Automated testing mapped to real-world threat models and MITRE ATLAS.

04

EDR Evasion Testing

Validate your endpoint detection against actual evasion techniques. Caldera + Atomic Red Team integration.

05

Cost-Per-Attack Metrics

Quantifiable risk. Know exactly what it costs an attacker to breach each surface. Prioritize what matters.

06

Modular Packs

Unix philosophy. Composable attack modules. Human-readable configs. Extend with your own capabilities.

Why not just use PyRIT / manual pentests?

Capability
ADCL
Legacy
Adaptive attack mutation
Multi-agent coordination
Autonomous campaign execution
Real-time defense bypass
Quantified risk metrics
partial
MITRE ATLAS mapping

// RELEASES

Demo builds. No warranty. Break things responsibly.

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