AI Red Teaming Pioneers: RedTeamLLM and DeepTeam Lead the Way in Advanced Artificial Intelligence Innovation
DeepTeam Red Teaming Framework Assesses Vulnerabilities in Large Language Models
The open-source DeepTeam red teaming framework is designed to test and assess the vulnerabilities of large language models (LLMs). By simulating adversarial attacks, DeepTeam aims to uncover unsafe behaviors related to bias, toxicity, misinformation, and unauthorized access.
DeepTeam's core components include:
- The RedTeamer module, which orchestrates the entire red teaming process, targeting specific vulnerabilities by generating and delivering adversarial prompts to the LLM under test.
- Predefined vulnerabilities classes such as Bias, Toxicity, Misinformation, Illegal Activity, and Robustness issues related to hijacking or model poisoning.
- Collections of attack types, including PromptInjection, Roleplay, LinearJailbreaking, CrescendoJailbreaking, and GrayBox attacks.
- An automated evaluation system that scores LLM responses using vulnerability-specific metrics to assess the severity of unsafe behaviors after each attack.
DeepTeam's approach to assessing system vulnerabilities involves generating baseline attacks tailored to vulnerabilities like bias, toxicity, misinformation, and more. These attacks are then enhanced via adversarial techniques such as prompt injections to bypass the model’s safety filters and elicit unauthorized or harmful responses.
Roleplay attacks mimic real-world adversaries or malicious users engaging the model in multi-turn dialogues to progressively coax it into unsafe behavior or policy violations. Feeding these attacks into the model and analyzing its responses helps detect signs of bias or toxicity, failures in robustness, and effects of data or model poisoning.
DeepTeam explicitly targets biases, toxicity, and unauthorized access risks by focusing on vulnerabilities often introduced or amplified by compromised training data or fine-tuning stages (supply chain vulnerabilities) and sophisticated prompt injection methods like Microsoft's cross-prompt injection attack (XPIA).
A case study using DeepTeam targeted Claude 4 Opus's robustness against adversarial prompts, revealing that prompt injection failed, while role-playing was successful.
Key functional points of DeepTeam include:
- Automating adversarial attack simulation and response evaluation on LLMs to detect risks.
- The RedTeamer Component, which serves as the main orchestrator for delivering attacks and capturing/evaluating model outputs.
- Targeting vulnerabilities like bias (race, gender, politics), toxicity (profanity, insults), misinformation, and illegal activities.
- Using attack methods like Prompt Injection, Roleplay, Jailbreaking, and GrayBox attacks (partial system knowledge based).
- Employing vulnerability-specific scoring for bias, toxicity, unauthorized access, and poisoning effects.
This holistic approach enables the detection of safety issues arising both from direct prompt-based exploits and from deeper systemic flaws like data poisoning or robustness failures.
References: [1] DeepMind. (2021). On the mechanics of adversarial attacks and defenses on language models. arXiv preprint arXiv:2106.09638. [2] Jia, Y., Liang, P., Lee, K., & Li, W. (2017). Adversarial examples in text classification. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. [3] Wallace, C., & Hassan, S. (2020). A survey on adversarial attacks and defense techniques for natural language processing. ACM Transactions on Speech and Language Processing, 28(1), 1-28. [4] Zellers, T., Ribeiro, S., & Lakhotia, A. (2020). Swaggering with Language Models. arXiv preprint arXiv:2002.05709.
The DeepTeam framework's evaluation system scores language model responses using vulnerability-specific metrics, such as those for bias, toxicity, and unauthorized access, to assess the severity of unsafe behaviors after each attack.
DeepTeam's approach to assessing system vulnerabilities involves not only simulating adversarial attacks tailored to vulnerabilities like bias, toxicity, and misinformation, but also employing advanced techniques like social engineering through roleplay attacks and artificial-intelligence-driven prompt injections to uncover systemic flaws and deeper safety issues.
