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1,065 papers
A federated learning framework that combines a Gaussian-mechanism differential privacy layer with a coordinate-wise trimmed-mean Byzantine-robust aggregation rule, evaluated on a simulated cross-institutional classification task resembling fraud and clinical-risk scoring.
A deployed system that scores an address by its position in a multi-chain transaction graph rather than by its presence in a list, and an adversarial harness of eight recurrent reinforcement-learned archetypes that passes an 8-criterion degeneracy audit and exposes a measured blind spot of the deployed heads against sy...
As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and infrastructure to train shared models without exposing raw sensor data. Yet the updates clients transmit still leak enough information to identify who sent them, which threa...
This work studies refusal directions through the training dynamics across refusal datasets and reveals that their brittleness is associated with repetitive refusal starts, which is linked to concentration of gradients and refusal features in a low-dimensional subspace.
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From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large language models (LLMs). However, large-scale web scraping to feed LLMs can affect site stability and raise legal, privacy, or ethics concerns. If website owners wish to limi...
The results show that context-aware security assessment is a practical complement to existing AE processes and can support safer and more responsible research artifact sharing.
The Model Context Protocol (MCP) widens the prompt injection attack surface of large language model applications to tool descriptions, parameter schemas, and tool outputs. Defenses for it are appearing quickly, but their reported figures are not comparable: each is evaluated on a corpus of its authors' construction, un...
A new benchmark to more comprehensively evaluate CUAs'misuse risks, CUAHarm, and explores using LMs to monitor CUAs'actions, finding monitoring unsafe computer-using actions is significantly harder than monitoring conventional unsafe chatbot responses.
In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained on residual stream activations at each transformer block. We find that refusal is linearly decodable well before the final layer, indicating that safety-relevant behavior...
This work presents Sentinel-RL, an agentic-SOC architecture that decouples topological reasoning from semantic reasoning, and contributes a reusable engineering pattern, a portable HPC deployment pattern, and an enterprise-readiness analysis covering false-positive economics, reversibility guarantees, audit compliance,...
For every coherent and sufficiently expressive finite syntactic system S, we prove the existence of at least one theorem that S cannot produce autonomously. The result is a metatheorem: it proves the existence of a theorem, and applies to every finite syntactic system - security mechanisms, AI systems, formal verifiers...
A patch similarity metric is introduced to detect memorized patches and new patch validation methods are developed that thoroughly evaluate both security and semantic correctness of agent patches for vulnerability patching.
Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity.
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.
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