AI-driven RedTeamLLM and DeepTeam spearhead advancements in AI red teaming techniques, pushing the boundaries of artificial intelligence in cybersecurity assessments.
DeepTeam and RedTeamLLM are innovative modular frameworks designed to test the resilience of AI systems against adversarial prompts. These tools simulate controlled adversarial attacks, exposing potential vulnerabilities such as social engineering and bias traps in AI behaviour.
The DeepTeam Framework
DeepTeam's red teaming workflow is automated, with four core components: vulnerabilities (like bias traps), adversarial attacks (such as social engineering prompts or manipulative inputs), the target AI system being tested, and metrics that evaluate how successfully the AI defends against these attacks.
The framework uses adversarial prompts to try to trigger outputs displaying the AI's bias or susceptibility to social engineering tactics. It then scores these outputs using safety metrics to assess the AI’s resilience.
Social Engineering Tests and Bias Traps
Social engineering tests typically involve adversarially crafted prompts designed to trick the AI into revealing protected information or performing disallowed actions. On the other hand, bias traps involve prompts that seek to elicit responses revealing prejudiced, one-sided, or harmful biases.
By combining these adversarial prompt methods, DeepTeam exposes and quantifies risks in AI behaviour before deployment, helping developers apply mitigations or guardrails.
The Linear Jailbreaking Attack
One example of a multi-turn attack in DeepTeam is the Linear Jailbreaking attack. This attack attempts to bypass safeguards over up to 15 conversation turns, testing the AI's resilience against persistent adversarial inputs.
RedTeamLLM: Simulating Goal-Oriented Exploitation
RedTeamLLM is a testing framework that simulates goal-oriented exploitation through autonomous agents. Its architecture includes a Launcher, RedTeamAgent, ADaPT Enhanced, Planner & Corrector, Memory Manager, and ReAct Terminal.
RedTeamLLM's execution involves four distinct stages: DAG generation and task ingestion, terminal interaction, memory logging, and a feedback loop. It uses memory management to learn and improve over time, narrowing possibilities to the right path.
Case Study: Evaluating Claude 4 Opus's Robustness
A case study was conducted using DeepTeam to evaluate Claude 4 Opus's robustness against adversarial prompts, targeting three major vulnerabilities: Bias (race, gender, religion). The study used the Linear Jailbreaking attack and a successful roleplay attack, which exploited Claude 4 Opus's weaknesses such as Academic Framing, Historial Roleplay, and Persona Trust.
The Code Provided
The code provided sets up an automated red-teaming framework for testing Language Models (LLMs). It targets vulnerabilities such as toxicity, bias, and unauthorized access, and includes attack modules like roleplay and prompt injection.
In summary, DeepTeam and RedTeamLLM are valuable tools for measuring and improving AI resilience specifically around social engineering manipulation and bias traps. They automate adversarial prompt generation, execute these prompts against the AI model, and evaluate the outputs for harmful or biased content, all while quantifying the AI’s failure or success in resisting these attacks.
[1] DeepTeam: Automated Red-Teaming Framework for Testing AI Resilience
[2] DeepTeam: A Modular Red-Teaming Framework for Testing AI Resilience
- The DeepTeam framework includes social engineering prompts as a type of adversarial attack, focusing on revealing AI susceptibility to deceptive tactics, which can be found in the encyclopedia under topics like 'artificial-intelligence' and 'adversarial attacks'.
- RedTeamLLM, on the other hand, tests AI systems using goal-oriented exploitation, simulating attack vectors such as the Linear Jailbreaking attack and roleplay attacks that exploit bias traps, making it a essential resource in understanding AI technology's vulnerabilities to AI-focused adversaries and in the study of artificial-intelligence and its security concerns.
