EUVD-2007-0864

Malware in sbrugna...

Why Software Signing (Still) Matters: Trust Boundaries in the Software Supply Chain

Software signing provides a formal mechanism for provenance by ensuring artifact integrity and verifying producer identity. It also imposes tooling and operational costs to implement in practice. In an era of centralized registries such as PyPI, npm, Maven Central, and Hugging Face, it is...

EUVD-2023-1011

Malicious code in bioql PyPI...

EUVD-2025-7727

Malicious code in bioql PyPI...

GitHub Mandates 2FA and Short-Lived Tokens to Strengthen npm Supply Chain Security

GitHub on Monday announced that it will be changing its authentication and publishing options "in the near future" in response to a recent wave of supply chain attacks targeting the npm ecosystem, including the Shai-Hulud attack. This includes steps to address threats posed by token abuse and...

Google Pixel 10 Adds C2PA Support to Verify AI-Generated Media Authenticity

Google on Tuesday announced that its new Google Pixel 10 phones support the Coalition for Content Provenance and Authenticity C2PA standard out of the box to verify the origin and history of digital content. To that end, support for C2PA's Content Credentials has been added to Pixel Camera and...

MirGuard: Towards a Robust Provenance-Based Intrusion Detection System against Graph Manipulation Attacks

Learning-based Provenance-based Intrusion Detection Systems PIDSes have become essential tools for anomaly detection in host systems due to their ability to capture rich contextual and structural information, as well as their potential to detect unknown attacks. However, recent studies have shown...

Can AI Keep a Secret? Contextual Integrity Verification: a Provable Security Architecture for LLMs

Large language models LLMs remain acutely vulnerable to prompt injection and related jailbreak attacks; heuristic guardrails rules, filters, LLM judges are routinely bypassed. We present Contextual Integrity Verification CIV, an inference-time security architecture that attaches cryptographically...

ProvX: Generating Counterfactual-Driven Attack Explanations for Provenance-Based Detection

Provenance graph-based intrusion detection systems are deployed on hosts to defend against increasingly severe Advanced Persistent Threat. Using Graph Neural Networks to detect these threats has become a research focus and has demonstrated exceptional performance. However, the widespread adoption...

AuthPrint: Fingerprinting Generative Models against Malicious Model Providers

Generative models are increasingly adopted in high-stakes domains, yet current deployments offer no mechanisms to verify the origin of model outputs. We address this gap by extending model fingerprinting techniques beyond the traditional collaborative setting to one where the model provider may a...

Google Launches OSS Rebuild to Expose Malicious Code in Widely Used Open-Source Packages

Google has announced the launch of a new initiative called OSS Rebuild to bolster the security of the open-source package ecosystems and prevent software supply chain attacks. "As supply chain attacks continue to target widely-used dependencies, OSS Rebuild gives security teams powerful data to...

CLIProv: a Contrastive Log-To-Intelligence Multimodal Approach for Threat Detection and Provenance Analysis

With the increasing complexity of cyberattacks, the proactive and forward-looking nature of threat intelligence has become more crucial for threat detection and provenance analysis. However, translating high-level attack patterns described in Tactics, Techniques, and Procedures TTP intelligence...

A Systematization of Security Vulnerabilities in Computer Use Agents

Computer Use Agents CUAs, autonomous systems that interact with software interfaces via browsers or virtual machines, are rapidly being deployed in consumer and enterprise environments. These agents introduce novel attack surfaces and trust boundaries that are not captured by traditional threat...

The Age of Sensorial Zero Trust: Why We Can No Longer Trust Our Senses

In a world where deepfakes and cloned voices are emerging as sophisticated attack vectors, organizations require a new security mindset: Sensorial Zero Trust. This article presents a scientific analysis of the need to systematically doubt information perceived through the senses, establishing...

ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines

We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and...

Few-Shot Learning-Based Cyber Incident Detection with Augmented Context Intelligence

In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more and more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents such as data breaches are on the rise. Many inherent attributes of cloud...

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems

Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats APTs, by exposing complex attack patterns. While recent systems combine graph neural networks GNNs with natural language processing NLP to capture structural and semantic features...

Attack Effect Model Based Malicious Behavior Detection

Traditional security detection methods face three key challenges: inadequate data collection that misses critical security events, resource-intensive monitoring systems, and poor detection algorithms with high false positive rates. We present FEAD Focus-Enhanced Attack Detection, a framework that...

StealthInk: a Multi-Bit and Stealthy Watermark for Large Language Models

Watermarking for large language models LLMs offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection b...