SEVulDet

SEVulDet SEVulDet is a semantics-enhanced deep learning-based framework that can accurately pinpoint vulnerability patterns by extracting, preserving, and learning more semantics. E-mail for communication: [email protected] Details of SEVulDet Recent years have seen increased attention to de...

Sec5GLoc

Sec5GLoc: Securing 5G Indoor Localization via Adversary-Resilient Deep Learning Architecture Our work addresses security and privacy challenges in 5G indoor localization by proposing an adversary-resilient deep learning architecture. Sec5GLoc combines Channel Impulse Response CIR fingerprinting...

attack-attention

Attacking Attention of Foundation Models Effectively Disrupts Downstream Tasks Official PyTorch implementation of the paper "Attacking Attention of Foundation Models Effectively Disrupts Downstream Tasks" , accepted in the Adversarial Machine Learning on Computer Vision: Foundation Models + X ADV...

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...

Parameters Efficient Language Model with Latent Cross-Attention for IoT Intrusion Detection

Language models have shown strong potential for intrusion detection, but their high memory and computational demands make them difficult to deploy in resource-constrained IoT environments. In this work, we propose a compact Transformer-based architecture that compresses the input into a small...

CVE-2026-93034

SGLang contains an arbitrary code execution vulnerability caused by the ZMQ message decoder unconditionally deserializing PickleWrapper payloads via pickle.loads in maybeunwrappickle without type allowlisting or authentication; this vulnerability persists via the msgpack path even when...

CVE-2026-93034

SGLang is affected by an arbitrary code execution vulnerability originating from the ZMQ message decoder . The root cause is the unconditional deserialization of PickleWrapper payloads using pickle.loads() within the _maybe_unwrap_pickle function, which lacks type allowlisting or authentication. ...

CVE-2026-93034 CVE-2026-93034

SGLang contains an arbitrary code execution vulnerability caused by the ZMQ message decoder unconditionally deserializing PickleWrapper payloads via pickle.loads in maybeunwrappickle without type allowlisting or authentication; this vulnerability persists via the msgpack path even when...

EUVD-2026-94989

SGLang contains an arbitrary code execution vulnerability caused by the ZMQ message decoder unconditionally deserializing PickleWrapper payloads via pickle.loads in maybeunwrappickle without type allowlisting or authentication; this vulnerability persists via the msgpack path even when...

CVE-2026-93034: Deserialization of Untrusted Data

SGLang contains an arbitrary code execution vulnerability caused by the ZMQ message decoder unconditionally deserializing PickleWrapper payloads via pickle.loads in maybeunwrappickle without type allowlisting or authentication; this vulnerability persists via the msgpack path even when...

PT-2026-108017

Name of the Vulnerable Software and Affected Versions SGLang affected versions not specified Description An arbitrary code execution issue exists due to the ZMQ message decoder unconditionally deserializing PickleWrapper payloads using the pickle.loads function within maybe unwrap pickle without...

Invisible Ink, Visible Lies: How Production Watermarking Causes LLMs to Hallucinate

Text watermarking helps identify AI-generated content, but its effect on factual reliability remains underexplored. In this paper, we study watermarking hallucination: factual errors induced or amplified by watermarking even when the required evidence is present in the context and the unwatermark...

CVE-2026-91073 Subscribe Forms 1.4.1 - 1.6.2 - Author+ Stored XSS via Attention Effect Form Setting

The Subscribe Forms WordPress plugin before 1.6.3 does not sanitise and escape one of its form settings before outputting it in a page, allowing authenticated users with the Author role and above to perform Stored Cross-Site Scripting attacks that execute in the browser of any visitor who views a...

CVE-2026-91073

The Subscribe Forms WordPress plugin (versions 1.4.1-1.6.2 ) is vulnerable to a Stored Cross-Site Scripting (XSS) attack. The root cause is the failure to properly sanitise and escape specific form settings before they are outputted on a page. This allows authenticated users with the Author role ...

CVE-2026-91073 Subscribe Forms 1.4.1 - 1.6.2 - Author+ Stored XSS via Attention Effect Form Setting

The Subscribe Forms WordPress plugin before 1.6.3 does not sanitise and escape one of its form settings before outputting it in a page, allowing authenticated users with the Author role and above to perform Stored Cross-Site Scripting attacks that execute in the browser of any visitor who views a...

Astra Linux – Vulnerability found in Linux 5.10, Linux 6.1, Linux, Linux 5.15

In the Linux kernel, the following vulnerability has been resolved: usb: typec: bus: verify partner exists in typecaltmodeattention Some USB hubs will negotiate DisplayPort Alt mode with the device. However, they will then negotiate a data role swap after entering the Alt mode. This data role swa...

SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center

Large language model LLM agents are increasingly proposed as autonomous SOC analysts, but two limitations make them unreliable at enterprise scale: a finite context window cannot hold a multi-thousand-host authentication graph, and free-form generation offers no guarantee that a recommended...

Circuit Discovery Helps Detect LLM Jailbreaking: A Mechanistic Interpretability Study

Despite extensive safety alignment, large language models LLMs remain vulnerable to jailbreak attacks that bypass safeguards to elicit harmful content. While prior work attributes this vulnerability to safety training limitations, the internal mechanisms by which LLMs process adversarial prompts...

BGA: A Noise-Immune Neural Distillation Framework for Malicious Signature Extraction in High-Entropy Encrypted Flows

To mitigate attention dilution in high-entropy TLS 1.3 flows, we propose BGA, a noise-immune neural distillation framework for encrypted threat intelligence.The methodology first employs Analysis of Variance ANOVA to decouple high-discriminatory control-plane features - specifically industrial...

Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks

Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt...