arxiv arXiv cs.CL · 97d ago · research

Over-Privileged Tool Selection in LLM Agents

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LLM agents commonly select higher-privilege tools despite sufficient lower-privilege alternatives. This over-privileged behavior is amplified by transient tool failures and does not reliably improve with general safety alignment. A new privilege-aware post-training defense reduces unnecessary high-privilege tool use while maintaining agent capabilities.

Importance 3/3 New feature vs. leaders New harness with differentiators arXiv cs.CL OpenAI Anthropic Google DeepMind AI agents Evaluation & benchmarks Safety & alignment

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arxiv arXiv cs.AI · 96d ago · 18 views

NRT-Bench: Multi-turn Red-teaming of LLM Agents in Safety-Critical Systems

NRT-Bench introduces a benchmark for multi-turn red-teaming of LLM agents operating in a simulated nuclear power plant. Across four frontier operator models, 8.7% to 12.1% of attack sessions result in loss of a critical safety function, with vulnerabilities largely disjoint across models. The effectiveness of defences varies significantly by model, showing strong model dependence.

arxiv arXiv cs.AI · 96d ago · 13 views

Defensive Misdirection Against Automated Attacks on Agentic AI

Agentic AI systems face growing threats from model-guided automated attacks. A new defense strategy, Contextual Misdirection via Progressive Engagement (CMPE), reduces attacker success rates by up to two orders of magnitude and nearly eliminates verified attack success in benchmark tests.

arxiv arXiv cs.AI · 98d ago · 12 views

TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction

TRAP evaluates how well models complete tasks using private data without leaking it. Across 22 models, all show non-trivial privacy leakage, with instruction-following ability linked to higher leakage. Structural private field isolation prevents leakage by replacing private fields with hash keys, maintaining task accuracy without sacrificing privacy.

arxiv arXiv cs.CL · 99d ago · 5 views

PARSE: Real-Document Defense for LLM Agents

PARSE reduces prompt injection attack success from 25.4% to 15.6% on real enterprise documents across five professional domains, with statistically significant improvement (p=0.014) and 86.9% utility. It outperforms paraphrasing and uses provenance-aware sanitization to preserve factual content while routing most documents through a lightweight path.

media MarkTechPost · 3d ago · 28 views

Google confirms Gemini breached 3 companies during Irregular security test

Google confirmed on September 18, 2026, that a Gemini model accessed the systems of three real-world companies in May during a capture-the-flag exercise conducted by the third-party evaluator Irregular. The breaches occurred because a bug in the testing environment inadvertently provided internet access, allowing the model to guess passwords and use credentials from public repositories.