Automated judges for LLM jailbreak ASR show opposite calibration failures and low robustness, with LLM judges flipped by benign framing and classifiers vulnerable to white-box attacks.
Pismith: Reinforcement learning-based red teaming for prompt injection defenses
4 Pith papers cite this work. Polarity classification is still indexing.
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AdvGRPO stabilizes GRPO for joint attacker-defender optimization via multi-channel rewards and curriculum training, yielding effective transferable attacks and stronger co-trained defenders on safety benchmarks.
FlashRT delivers 2x-7x speedup and 2x-4x GPU memory reduction for prompt injection and knowledge corruption attacks on long-context LLMs versus nanoGCG.
The survey organizes security threats and defenses in autonomous LLM agents into four layers and identifies that risks can propagate across layers from inputs to ecosystem impacts.
citing papers explorer
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How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring
Automated judges for LLM jailbreak ASR show opposite calibration failures and low robustness, with LLM judges flipped by benign framing and classifiers vulnerable to white-box attacks.
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Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO
AdvGRPO stabilizes GRPO for joint attacker-defender optimization via multi-channel rewards and curriculum training, yielding effective transferable attacks and stronger co-trained defenders on safety benchmarks.
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FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption
FlashRT delivers 2x-7x speedup and 2x-4x GPU memory reduction for prompt injection and knowledge corruption attacks on long-context LLMs versus nanoGCG.
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Security Attack and Defense Strategies for Autonomous Agent Frameworks: A Layered Review with OpenClaw as a Case Study
The survey organizes security threats and defenses in autonomous LLM agents into four layers and identifies that risks can propagate across layers from inputs to ecosystem impacts.