Red-TeamingSecurityMulti-AgentAdversarial Testing
Multi-Agent Red-Teaming Harness
Adversarial testing through agent collaboration
The Problem
Traditional red-teaming of AI systems relies on human creativity and manual testing. This approach doesn't scale—humans can't systematically explore the vast space of possible adversarial inputs.
Single-agent automated red-teaming tends to exploit the same patterns repeatedly. Diverse attack strategies require diverse thinking, which emerges more naturally from multi-agent collaboration.
Visual Architecture
Approach
Specialized Attack Agents: Different agents specialize in different attack modalities—prompt injection, jailbreak attempts, data poisoning, model extraction, etc.
Attack Coordination: A coordination layer synthesizes findings across agents, identifying compound vulnerabilities that span attack types.
Adaptive Strategy Evolution: Successful attack patterns are shared across agents; failed approaches are analyzed to understand why they failed.
Defense Integration: Discovered vulnerabilities are automatically converted into defense test cases for the target system.
Ethical Considerations
Dual Use: Red-teaming tools can be used both defensively and offensively. How do we prevent misuse?
Vulnerability Disclosure: When new vulnerabilities are discovered, what are the disclosure obligations? Immediate public disclosure may cause harm; delayed disclosure may leave systems vulnerable.
Attack Creativity Bounds: Should we limit how creative adversarial agents can be? More creative attacks provide better coverage but may discover attacks that are harmful even to know about.
Target System Consent: Red-teaming without consent raises ethical issues. The framework must ensure appropriate authorization.
Architecture
- ▸Attack Agent Pool: Configurable set of specialized adversarial agents with different capabilities
- ▸Target Interface: Sandboxed environment for safely probing target systems
- ▸Vulnerability Database: Structured storage for discovered vulnerabilities with severity scoring
- ▸Coordination Engine: Multi-agent communication and strategy synthesis layer
- ▸Defense Bridge: Automatic generation of defensive test cases from discovered vulnerabilities
Key Insights
- 1Multi-agent diversity produces more comprehensive vulnerability coverage than single-agent approaches
- 2The best defense is informed by realistic offense; red-teaming should directly feed defensive improvements
- 3Attack pattern sharing between agents accelerates discovery but requires careful coordination to avoid redundancy
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Interface with the System