SOTAVerified

Red Teaming

Papers

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Showing 1–25 of 251 papers

TitleStatusHype
RabakBench: Scaling Human Annotations to Construct Localized Multilingual Safety Benchmarks for Low-Resource LanguagesCode0
STACK: Adversarial Attacks on LLM Safeguard Pipelines—0
We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent SystemsCode0
Effective Red-Teaming of Policy-Adherent Agents—0
GenBreak: Red Teaming Text-to-Image Generators Using Large Language Models—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 Red Teaming Roadmap Towards System-Level Safety—0
Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges—0
A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming—0
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data SynthesisCode0
SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models?—0
CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring—0
RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS EnvironmentsCode1
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
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teamingCode1
Towards medical AI misalignment: a preliminary study—0
RRTL: Red Teaming Reasoning Large Language Models in Tool Learning—0
EVA: Red-Teaming GUI Agents via Evolving Indirect Prompt Injection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SUDOAttack Success Rate41—Unverified