D-Scan

When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse Official implementation of SIGIR 2026 paper When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse. D-SCAN is an analysis framework for detecting document poisoning attacks in RAG...

CVE-2026-116180

A flaw was found in vLLM. A remote client sharing the inference engine can trigger a Denial of Service DoS by submitting requests with prompt log probabilities enabled against a cached prefix. Because uninitialized memory is allocated for log probability tensors, cached token positions remain...

TUPLE-Bench: Evaluating and Mitigating Privacy-Utility Trade-Offs in Typed Decision Models

Typed decision models often return more than a selected category. They also expose probabilities for each candidate and, sometimes, a separate confidence score. These fields support downstream decisions but may reveal information beyond a recipient's authorization. We introduce TUPLE-Bench Typed...

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world scenarios that can amplify backdoor threats. This paper presents the first in-depth investigation of how the dataset...

An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as the...

JULI: Jailbreak Large Language Models by Self-Introspection

Large Language Models LLMs are trained with safety alignment to prevent generating malicious content. Although some attacks have highlighted vulnerabilities in these safety-aligned LLMs, they typically have limitations, such as necessitating access to the model weights or the generation process...

getRandomTokenIdFromFund yields wrong probabilities for ERC1155

Handle @cmichelio Vulnerability details Vulnerability Details NFTXVaultUpgradeable.getRandomTokenIdFromFund does not work with ERC1155 as it does not take the deposited quantity1155 into account. Impact Assume tokenId0 has a count of 100, and tokenId1 has a count of 1. Then getRandomId would have...