Revisiting UNC3886 Tactics to Defend Against Present Risk

We examine the past tactics used by UNC3886 to gain insight on how to best strengthen defenses against the ongoing and emerging threats of this APT group...

Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy

This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks QNNs in the Noisy Intermediate-Scale Quantum NISQ era. In this paper, we present an integrated framework that leverages and combines existing approaches to enhance...

Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition

Recent advances have enabled LLM-powered AI agents to autonomously execute complex tasks by combining language model reasoning with tools, memory, and web access. But can these systems be trusted to follow deployment policies in realistic environments, especially under attack? To investigate, we...

PT-2025-30891 · Unknown · Openblow Whistleblowing Platform

Name of the Vulnerable Software and Affected Versions: OpenBlow whistleblowing platform affected versions not specified Description: A client-side security misconfiguration exists due to the absence of critical HTTP response headers, including Content-Security-Policy, Referrer-Policy,...

LoRA-Leak: Membership Inference Attacks against LoRA Fine-Tuned Language Models

Language Models LMs typically adhere to a "pre-training and fine-tuning" paradigm, where a universal pre-trained model can be fine-tuned to cater to various specialized domains. Low-Rank Adaptation LoRA has gained the most widespread use in LM fine-tuning due to its lightweight computational cost...

GATEBLEED: Exploiting On-Core Accelerator Power Gating for High Performance and Stealthy Attacks on AI

As power consumption from AI training and inference continues to increase, AI accelerators are being integrated directly into the CPU. Intel's Advanced Matrix Extensions AMX is one such example, debuting on the 4th generation Intel Xeon Scalable CPU. We discover a timing side and covert channel,...

CVE-2025-0664

A locally authenticated, privileged user can craft a malicious OpenSSL configuration file, potentially leading the agent to load an arbitrary local library. This may impair endpoint defenses and allow the attacker to achieve code execution with SYSTEM-level privileges...

CTEM vs ASM vs Vulnerability Management: What Security Leaders Need to Know in 2025

The modern-day threat landscape requires enterprise security teams to think and act beyond traditional cybersecurity measures that are purely passive and reactive, and in most cases, ineffective against emerging threats and sophisticated threat actors. Prioritizing cybersecurity means implementin...

Space Cybersecurity Testbed: Fidelity Framework, Example Implementation, and Characterization

Cyber threats against space infrastructures, including satellites and systems on the ground, have not been adequately understood. Testbeds are important to deepen our understanding and validate space cybersecurity studies. The state of the art is that there are very few studies on building...

Preventing Zero-Click AI Threats: Insights from EchoLeak

A zero-click exploit called EchoLeak reveals how AI assistants like Microsoft 365 Copilot can be manipulated to leak sensitive data without user interaction. This entry breaks down how the attack works, why it matters, and what defenses are available to proactively mitigate this emerging AI-nativ...

May I Have Your Attention? Breaking Fine-Tuning Based Prompt Injection Defenses Using Architecture-Aware Attacks

A popular class of defenses against prompt injection attacks on large language models LLMs relies on fine-tuning the model to separate instructions and data, so that the LLM does not follow instructions that might be present with data. There are several academic systems and production-level...

S-Leak: Leakage-Abuse Attack against Efficient Conjunctive SSE Via S-Term Leakage

Conjunctive Searchable Symmetric Encryption CSSE enables secure conjunctive searches over encrypted data. While leakage-abuse attacks LAAs against single-keyword SSE have been extensively studied, their extension to conjunctive queries faces a critical challenge: the combinatorial explosion of...

Holographic Projection and Cyber Attack Surface: a Physical Analogy for Digital Security

This article presents an in-depth exploration of the analogy between the Holographic Principle in theoretical physics and cyber attack surfaces in digital security. Building on concepts such as black hole entropy and AdS/CFT duality, it highlights how complex infrastructures project their...

Advancing Jailbreak Strategies: a Hybrid Approach to Exploiting LLM Vulnerabilities and Bypassing Modern Defenses

The advancement of Pre-Trained Language Models PTLMs and Large Language Models LLMs has led to their widespread adoption across diverse applications. Despite their success, these models remain vulnerable to attacks that exploit their inherent weaknesses to bypass safety measures. Two primary...

Adversarial Threats in Quantum Machine Learning: a Survey of Attacks and Defenses

Quantum Machine Learning QML integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Noisy Intermediate-Scale Quantum NISQ era. This chapter examines...

Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions

The rapid advancement of 6G wireless networks, IoT, and edge computing has significantly expanded the cyberattack surface, necessitating more intelligent and adaptive vulnerability detection mechanisms. Traditional security methods, while foundational, struggle with zero-day exploits, adversarial...

Network Structures As an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct th...

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT

Address Resolution Protocol ARP spoofing attacks severely threaten Internet of Things IoT networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a...

Investigating Vulnerabilities and Defenses against Audio-Visual Attacks: a Comprehensive Survey Emphasizing Multimodal Models

Multimodal large language models MLLMs, which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computation...

Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems

The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems DDLSs have emerged, offering input-adaptive computation to optimize runtim...