ARMOR 2025 is a new benchmark with 519 doctrinally grounded prompts across a 12-category OODA-based taxonomy that reveals critical safety gaps in 21 commercial LLMs for military applications.
A Safe Harbor for AI Evaluation and Red Teaming
6 Pith papers cite this work. Polarity classification is still indexing.
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Interviews with 28 AIG-SC creators show motivations spanning sexual exploration, creative expression, technical experimentation, and occasional production of non-consensual intimate imagery.
CREST-Search is a red-teaming framework that crafts seemingly benign search queries to induce unsafe citations from web-augmented LLMs, backed by a new WebSearch-Harm dataset for fine-tuning a specialized attacker model.
Open-world evaluations using qualitative review of real-world tasks can give earlier warnings of frontier AI capabilities than automated benchmarks, as demonstrated by an AI agent publishing a simple iOS app with one minor human fix.
JailbreakBench supplies an evolving set of jailbreak prompts, a 100-behavior dataset aligned with usage policies, a standardized evaluation framework, and a leaderboard to enable comparable assessments of attacks and defenses on LLMs.
SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.
citing papers explorer
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ARMOR 2025: A Military-Aligned Benchmark for Evaluating Large Language Model Safety Beyond Civilian Contexts
ARMOR 2025 is a new benchmark with 519 doctrinally grounded prompts across a 12-category OODA-based taxonomy that reveals critical safety gaps in 21 commercial LLMs for military applications.
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"Unlimited Realm of Exploration and Experimentation": Methods and Motivations of AI-Generated Sexual Content Creators
Interviews with 28 AIG-SC creators show motivations spanning sexual exploration, creative expression, technical experimentation, and occasional production of non-consensual intimate imagery.
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When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models
CREST-Search is a red-teaming framework that crafts seemingly benign search queries to induce unsafe citations from web-augmented LLMs, backed by a new WebSearch-Harm dataset for fine-tuning a specialized attacker model.
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Open-World Evaluations for Measuring Frontier AI Capabilities
Open-world evaluations using qualitative review of real-world tasks can give earlier warnings of frontier AI capabilities than automated benchmarks, as demonstrated by an AI agent publishing a simple iOS app with one minor human fix.
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JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
JailbreakBench supplies an evolving set of jailbreak prompts, a 100-behavior dataset aligned with usage policies, a standardized evaluation framework, and a leaderboard to enable comparable assessments of attacks and defenses on LLMs.
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SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.