SOTAVerified

Red Teaming

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 251 papers

TitleStatusHype
Effective Red-Teaming of Policy-Adherent Agents—0
Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models—0
RedDebate: Safer Responses through Multi-Agent Red Teaming DebatesCode0
RedRFT: A Light-Weight Benchmark for Reinforcement Fine-Tuning-Based Red TeamingCode0
BitBypass: A New Direction in Jailbreaking Aligned Large Language Models with Bitstream CamouflageCode0
Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act—0
A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming—0
A Red Teaming Roadmap Towards System-Level Safety—0
Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges—0
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data SynthesisCode0
CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring—0
SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models?—0
Red-Teaming Text-to-Image Systems by Rule-based Preference Modeling—0
Capability-Based Scaling Laws for LLM Red-TeamingCode0
GhostPrompt: Jailbreaking Text-to-image Generative Models based on Dynamic Optimization—0
Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation—0
Towards medical AI misalignment: a preliminary study—0
RRTL: Red Teaming Reasoning Large Language Models in Tool Learning—0
"Haet Bhasha aur Diskrimineshun": Phonetic Perturbations in Code-Mixed Hinglish to Red-Team LLMs—0
Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents—0
EVA: Red-Teaming GUI Agents via Evolving Indirect Prompt Injection—0
Soft Prompts for Evaluation: Measuring Conditional Distance of CapabilitiesCode0
CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models—0
LARGO: Latent Adversarial Reflection through Gradient Optimization for Jailbreaking LLMs—0
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks SafetyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SUDOAttack Success Rate41—Unverified