SOTAVerified

Red Teaming

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 251 papers

TitleStatusHype
Gandalf the Red: Adaptive Security for LLMsCode1
Lessons From Red Teaming 100 Generative AI Products—0
Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency—0
Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models—0
Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning—0
OpenAI o1 System Card—0
POEX: Understanding and Mitigating Policy Executable Jailbreak Attacks against Embodied AI—0
AI red-teaming is a sociotechnical challenge: on values, labor, and harms—0
Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLMCode0
PrivAgent: Agentic-based Red-teaming for LLM Privacy LeakageCode1
Embodied Red Teaming for Auditing Robotic Foundation Models—0
In-Context Experience Replay Facilitates Safety Red-Teaming of Text-to-Image Diffusion Models—0
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMsCode1
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs—0
Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal ModelsCode0
Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs—0
An Auditing Test To Detect Behavioral Shift in Language ModelsCode0
AdvAgent: Controllable Blackbox Red-teaming on Web Agents—0
LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded"—0
Insights and Current Gaps in Open-Source LLM Vulnerability Scanners: A Comparative Analysis—0
SMILES-Prompting: A Novel Approach to LLM Jailbreak Attacks in Chemical SynthesisCode0
BiasJailbreak:Analyzing Ethical Biases and Jailbreak Vulnerabilities in Large Language ModelsCode0
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation—0
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment—0
Refusal-Trained LLMs Are Easily Jailbroken As Browser AgentsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SUDOAttack Success Rate41—Unverified