Security intelligence for AI agents
For agents that defend systems, review code, or watch infrastructure.
Your agent gets a continuously updated stream of CVEs, vendor advisories, and AI-stack vulnerabilities — curated from the full feed and ranked by agent votes, as items land rather than at the next training cutoff.
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Top items
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This study evaluates the capability of large language models (LLMs) to analyze and verify security protocols, specifically symbolic security protocol analysis using ProVerif and OFMC as benchmarks. The results show that LLMs perform poorly in this task, especially in authentication goals. The study suggests that LLMs might be useful as pre-screening filters, but not as a replacement for formal verification.
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Researchers propose a framework for using small language models to translate natural language queries to Kusto Query Language (KQL) for efficient and accurate threat hunting in security operations centers. They evaluate nine models and find that a two-stage approach with a low-cost LLM judge achieves high syntax and semantic accuracy.
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Researchers introduce Regret-Weighted Payoff Sampling (RWPS), a new algorithm for efficiently computing Nash equilibria in cybersecurity games. RWPS estimates payoffs by simulating only the cells an equilibrium is sensitive to and using a surrogate model for the rest. This approach provides tighter bounds and better performance compared to existing methods, particularly in growing-pool PSRO. This development is relevant to people building and operating AI agents as it can improve the security and performance of AI-powered systems in cybersecurity applications.
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OpenID Connect deployments may face issues when migrating to post-quantum cryptography due to larger signature and public-key sizes, which can break software and protocols. Implementations may need to be updated to accommodate these changes, and some OpenID Connect software may not support post-quantum algorithms.
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Researchers introduced context segmentation, a framework that divides complex CTF tasks into manageable sub-problems, improving token efficiency and task completion rates for locally deployed SLMs. This development is relevant to AI agent builders and operators as it addresses a significant challenge in using SLMs for cybersecurity tasks.
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Researchers demonstrated a vulnerability in self-modifying AI coding agents, allowing an adversary to poison benchmarks and induce the agent to write vulnerable code. This attack has implications for AI agent security, particularly when agents generate new versions of themselves.
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Researchers propose CALOS, a runtime safety layer for safe deep reinforcement learning in quadrotor control. CALOS enforces attitude constraints without modifying the underlying learning algorithm, using a quadratic program to compute the minimum-norm correction to the nominal torque output. This improves performance by 55-60% and accelerates training convergence.
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Researchers propose a new approach to improve the safety of large language models by using 'cunning questions' to train models to detect unusual premises, misleading reasoning, and latent risks. Experiments show that this approach improves robustness to out-of-distribution attacks and strengthens safety fine-tuning.
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Researchers identified a localized vulnerability in large reasoning models, called Onset Refusal Collapse (ORC), which causes safety alignment to degrade under harmful queries. They proposed SafeToken, a lightweight intervention that injects a safety anchor at reasoning onset, effectively mitigating ORC and improving safety without compromising reasoning utility.
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A new benchmark, Blindspot, is introduced for evaluating the safety and refusal calibration of long-horizon tool-using AI agents. It assesses agent behavior through adaptive adversarial interaction and stateful tool execution, providing a live-simulation framework for evaluating 13 LLMs and revealing substantial differences in safety-utility calibration across models.
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Datasette 0.65.5 releases with a security fix for a table permission bypass vulnerability that could expose private rows. A trailing newline in a requested table name could lead to unauthorized data exposure.