CVE-2025-53630

llama.cpp is an inference of several LLM models in C/C++. Integer Overflow in the ggufinitfromfileimpl function in ggml/src/gguf.cpp can lead to Heap Out-of-Bounds Read/Write. This vulnerability is fixed in commit 26a48ad699d50b6268900062661bd22f3e792579...

The Prompt as a Rulebook - Guiding LLM Agents Beyond Basic Instructions

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Phishing Detection in the Gen-AI Era: Quantized LLMs Vs Classical Models

Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning ML, Deep Learning DL, and quantized...

Can Large Language Models Improve Phishing Defense? A Large-Scale Controlled Experiment on Warning Dialogue Explanations

Phishing has become a prominent risk in modern cybersecurity, often used to bypass technological defences by exploiting predictable human behaviour. Warning dialogues are a standard mitigation measure, but the lack of explanatory clarity and static content limits their effectiveness. In this pape...

Securing the Frontier - Navigating Security in LLM-Integrated Systems

In the previous parts of this series, we've explored the exciting new ways Large Language Models LLMs can integrate with APIs and act as intelligent As we integrate LLMs deeper into our applications, the attack surface naturally expands...

Understanding Malware Propagation Dynamics through Scientific Machine Learning

Accurately modeling malware propagation is essential for designing effective cybersecurity defenses, particularly against adaptive threats that evolve in real time. While traditional epidemiological models and recent neural approaches offer useful foundations, they often fail to fully capture the...

Inside the AI Threat Landscape: From Jailbreaks to Prompt Injections and Agentic AI Risks

AI has officially moved out of the novelty phase. What began with people messing around with LLM-powered GenAI tools for content creation has rapidly evolved into a complex web of agentic AI systems that form a critical part of the modern corporate landscape. However, this transformation has give...

CVE-2025-40742

A vulnerability has been identified in SIPROTEC 5 6MD84 CP300 All versions V11.0, SIPROTEC 5 6MD85 CP200 All versions, SIPROTEC 5 6MD85 CP300 All versions V11.0, SIPROTEC 5 6MD86 CP200 All versions, SIPROTEC 5 6MD86 CP300 All versions V11.0, SIPROTEC 5 6MD89 CP300 All versions V11.0, SIPROTEC 5...

CVE-2023-52236

A vulnerability has been identified in RUGGEDCOM i800 All versions, RUGGEDCOM i801 All versions, RUGGEDCOM i802 All versions, RUGGEDCOM i803 All versions, RUGGEDCOM M2100 All versions, RUGGEDCOM M2200 All versions, RUGGEDCOM M969 All versions, RUGGEDCOM RMC30 All versions, RUGGEDCOM RMC8388 V4.X...

Enter, Exit, Page Fault, Leak: Testing Isolation Boundaries for Microarchitectural Leaks

CPUs provide isolation mechanisms like virtualization and privilege levels to protect software. Yet these focus on architectural isolation while typically overlooking microarchitectural side channels, exemplified by Meltdown and Foreshadow. Software must therefore supplement architectural defense...

Bridging AI and Software Security: a Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms

Large Language Model LLM agents face security vulnerabilities spanning AI-specific and traditional software domains, yet current research addresses these separately. This study bridges this gap through comparative evaluation of Function Calling architecture and Model Context Protocol MCP deployme...

CAVGAN: Unifying Jailbreak and Defense of LLMs Via Generative Adversarial Attacks on Their Internal Representations

Security alignment enables the Large Language Model LLM to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have isolated LLM jailbreak attacks and defenses. We analyze the security protection...

HPESBCR04892 rev.1 - Certain HPE ProLiant Cray Servers Using Certain AMD EPYC Processors, AMD-SB-7029: AMD Transient Scheduler Attacks, Multiple Vulnerabilities

Potential Security Impact: Local: Disclosure of Privileged Information, Disclosure of Sensitive Information SUPPORTED SOFTWARE VERSIONS: ONLY impacted versions are listed. HPE Cray EX235a Accelerator Blade - Prior to 2.1.0 in HFP 25.2.1 HPE Cray EX235n Server - Prior to 1.5.3 in HFP 25.5.0 HPE Cr...

Efficient Unlearning with Privacy Guarantees

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning ML models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data...

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation

Large Language Models LLMs have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressi...

LIFT: Automating Symbolic Execution Optimization with Large Language Models for AI Networks

Dynamic Symbolic Execution DSE is a key technique in program analysis, widely used in software testing, vulnerability discovery, and formal verification. In distributed AI systems, DSE plays a crucial role in identifying hard-to-detect bugs, especially those arising from complex network...

Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions

Large Language Models LLMs have revolutionized various fields with their exceptional capabilities in understanding, processing, and generating human-like text. This paper investigates the potential of LLMs in advancing Network Intrusion Detection Systems NIDS, analyzing current challenges,...

SoK: a Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments

As mobile computing becomes central to digital interaction, researchers have turned their attention to adaptive authentication for its real-time, context- and behavior-aware verification capabilities. However, many implementations remain fragmented, inconsistently apply intelligent techniques, an...

Emergent Misalignment As Prompt Sensitivity: a Research Note

Betley et al. 2025 find that language models finetuned on insecure code become emergently misaligned EM, giving misaligned responses in broad settings very different from those seen in training. However, it remains unclear as to why emergent misalignment occurs. We evaluate insecure models across...

Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAG

Malware Family Classification MFC aims to identify the fine-grained family e.g., GuLoader or BitRAT to which a potential malware sample belongs, in contrast to malware detection or sample classification that predicts only an Yes/No. Accurate family identification can greatly facilitate automated...