arXiv:2608. 11423v1 Announce Type: new Abstract: Robust comparisons of federated aggregation methods require joint consideration of predictive performance, threat definitions, metric semantics, and execution provenance.
By Soumya Mazumdar, Vineet Kumar Rakesh, Tapas Samanta
arXiv:2607. 07474v1 Announce Type: cross Abstract: Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not.
By Harry Owiredu-Ashley
The paper investigates the reliability of ranking tables produced by small-sample evaluations of large language models (LLMs). Using LLM‑inferred prompt structure across eight model variants, the authors find that prompt‑structure recovery is highly unstable, with only the bottom of the ranking consistently reproducible. They demonstrate that standard evaluation practices can misrepresent model performance and propose reporting practices to improve transparency.
By Dipankar Sarkar
arXiv:2607. 16357v1 Announce Type: cross Abstract: Security rating platforms summarize externally observable cyber exposure and are expected to help organizations prioritize remediation.
By Nada Hanad, Mehdi Acheli, Ali NourEldin, Mohamed Sellami, Walid Gaaloul
arXiv:2609.36570v1 Announce Type: cross
Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...
By Mark Russinovich
arXiv:2609.17394v1 Announce Type: cross
Abstract: Small differences on coding-agent leaderboards are often read as an ordering of systems. We audit whether the published verdicts support this reading...
By Fengshuo Liu, Ying Liu, Ruize Sun, Lie Luo, Siyuan Guo