SOTAVerified

Red Teaming

Papers

Showing 151–175 of 251 papers

TitleStatusHype
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models—0
OpenAI o1 System Card—0
Can Language Models be Instructed to Protect Personal Information?—0
The Promise and Peril of Artificial Intelligence -- Violet Teaming Offers a Balanced Path Forward—0
Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback—0
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle—0
Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models—0
POEX: Understanding and Mitigating Policy Executable Jailbreak Attacks against Embodied AI—0
Predictive Red Teaming: Breaking Policies Without Breaking Robots—0
Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models—0
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems—0
Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming—0
Purple-teaming LLMs with Adversarial Defender Training—0
Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models—0
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models—0
AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration—0
When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines—0
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations—0
RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent—0
Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming—0
Automated Red Teaming with GOAT: the Generative Offensive Agent Tester—0
Towards medical AI misalignment: a preliminary study—0
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SUDOAttack Success Rate41—Unverified