#safety-utility-tradeoff News & Analysis

2 articles tagged with #safety-utility-tradeoff. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBearisharXiv – CS AI · May 127/10

🧠

Why Do Aligned LLMs Remain Jailbreakable: Refusal-Escape Directions, Operator-Level Sources, and Safety-Utility Trade-off

Researchers identify Refusal-Escape Directions (RED) as mathematical perturbation vectors that explain why aligned LLMs remain vulnerable to jailbreaks. The study reveals structural vulnerabilities arise from fundamental trade-offs between safety mechanisms and model utility, with normalization and residual connections as key exploitable components.

AIBullisharXiv – CS AI · May 97/10

🧠

SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety

SafeHarbor is a new framework that enhances Large Language Model agent safety by using hierarchical memory and context-aware defense rules to prevent harmful tool use while maintaining utility on benign tasks. The system achieves 93%+ refusal rates against malicious requests while preserving 63.6% performance on legitimate tasks, addressing a critical trade-off in AI safety.

🧠 GPT-4