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adversarial robustness

topic3 events
papersTODAY 04:00 UTC

Admissable: Training RL Agents to Withstand Adversarial Missing Features

A new arXiv paper introduces Admissable, a training approach for reinforcement learning agents that must keep operating safely when an adversary deliberately removes or withholds input features. The work targets real-world deployments where sensor dropouts or tampered observations can degrade decision quality. It frames adversarial feature missingness as a distinct safety problem for RL rather than standard robustness to noise.

reinforcement-learning/adversarial-robustness/safety/arxiv
papersTODAY 04:00 UTC

Study identifies repetition-induced label flips in LLM guardrail classifiers

A new arXiv paper describes "overflip," a failure mode where guardrail models used to screen malicious prompts and responses change their classification when input context is repeated. The authors focus on lightweight Transformer-based guardrails, such as DeBERTa variants, which are common in latency-sensitive deployments and are trained on short contexts. The work suggests these compact classifiers can be unreliable under repeated or padded input.

safety/guardrails/research/llm-security