BadSkill: Backdoor Attacks on Agent Skills Via Model-In-Skill Poisoning

Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their execution logic. This creates a supply-chain risk that is not captured by prompt injection or ordinary plugin misuse: a third-party skill may appear...

Automating Cloud Security and Forensics through a Secure-By-Design Generative AI Framework

As cloud environments become increasingly complex, cybersecurity and forensic investigations must evolve to meet emerging threats. Large Language Models LLMs have shown promise in automating log analysis and reasoning tasks, yet they remain vulnerable to prompt injection attacks and lack forensic...

A Tsetlin Machine-Driven Intrusion Detection System for Next-Generation IoMT Security

The rapid adoption of the Internet of Medical Things IoMT is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and...

Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models

Large language models LLMs increasingly rely on explicit chain-of-thought CoT reasoning to solve complex tasks, yet the safety of the reasoning process itself remains largely unaddressed. Existing work on LLM safety focuses on content safety--detecting harmful, biased, or factually incorrect...

Spinning complex ideas into clear docs with Kri Dontje

Welcome back! This week, we're shining a spotlight on Kri Dontje, a technical writer who's become an essential voice in making Cisco Talos' work understandable for a wide audience. With a background in technical communications and a career that began at a small startup, Kri discusses the importan...

Manual Processes Are Putting National Security at Risk

Why automating sensitive data transfers is now a mission-critical priority More than half of national security organizations still rely on manual processes to transfer sensitive data, according to The CYBER360: Defending the Digital Battlespace report. This should alarm every defense and governme...

Can You Tell It'S AI? Human Perception of Synthetic Voices in Vishing Scenarios

Large Language Models and commercial speech synthesis systems now enable highly realistic AI-generated voice scams vishing, raising urgent concerns about deception at scale. Yet it remains unclear whether individuals can reliably distinguish AI-generated speech from human-recorded voices in...

A Scan-Based Analysis of Internet-Exposed IoT Devices Using Shodan Data

An open measurement problem in IoT security is whether scan-observable network configurations encode population-level exposure risk beyond individual devices. An analysis of internet-exposed IoT endpoints using a controlled multi-country sample from Shodan Search and Shodan InternetDB, selecting...

Inference-Time Backdoors Via Hidden Instructions in LLM Chat Templates

Open-weight language models are increasingly used in production settings, raising new security challenges. One prominent threat in this context is backdoor attacks, in which adversaries embed hidden behaviors in language models that activate under specific conditions. Previous work has assumed th...

Human-Centered Explainability in AI-Enhanced UI Security Interfaces: Designing Trustworthy Copilots for Cybersecurity Analysts

Artificial intelligence AI copilots are increasingly integrated into enterprise cybersecurity platforms to assist analysts in threat detection, triage, and remediation. However, the effectiveness of these systems depends not only on the accuracy of underlying models but also on the degree to whic...

Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework

The rapid expansion of low-altitude economy Internet of Things LAE-IoT networks has created unprecedented security challenges due to dynamic three-dimensional mobility patterns, distributed autonomous operations, and severe resource constraints. Traditional intrusion detection systems designed fo...

Constructing Multi-Label Hierarchical Classification Models for MITRE ATT&CK Text Tagging

MITRE ATT&CK is a cybersecurity knowledge base that organizes threat actor and cyber-attack information into a set of tactics describing the reasons and goals threat actors have for carrying out attacks, with each tactic having a set of techniques that describe the potential methods used in these...

Many Hands Make Light Work: An LLM-Based Multi-Agent System for Detecting Malicious PyPI Packages

Malicious code in open-source repositories such as PyPI poses a growing threat to software supply chains. Traditional rule-based tools often overlook the semantic patterns in source code that are crucial for identifying adversarial components. Large language models LLMs show promise for software...

Malware Classification Using Diluted Convolutional Neural Network with Fast Gradient Sign Method

Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and sophistication, the mitigation and detection of these malicious...

CVE-2019-2254

Position determination accuracy may be degraded due to wrongly decoded information in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in MDM9150, MDM9206, MDM9607, MDM9615,...

Jailbreaking Attacks Vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race?

As large language models LLMs are increasingly deployed, ensuring their safe use is paramount. Jailbreaking, adversarial prompts that bypass model alignment to trigger harmful outputs, present significant risks, with existing studies reporting high success rates in evading common LLMs. However,...

Evaluating MCC for Low-Frequency Cyberattack Detection in Imbalanced Intrusion Detection Data

In many real-world network environments, several types of cyberattacks occur at very low rates compared to benign traffic, making them difficult for intrusion detection systems IDS to detect reliably. This imbalance causes traditional evaluation metrics, such as accuracy, to often overstate model...

Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline

Prompt injection and jailbreaking attacks pose persistent security challenges to large language model LLM-based systems. We present an efficient and systematically evaluated defense architecture that mitigates these threats through a lightweight, multi-stage pipeline. Its core component is a...

PT-2025-52541

Name of the Vulnerable Software and Affected Versions Delphix Continuous Compliance versions 2025.3.0 and above Description An issue exists due to incorrect handling of End-of-Record EOR characters in delimited files. Specifically, an incorrect EOR configuration can lead to inaccurate parsing,...

SUSE CVE-2025-68313

In the Linux kernel, the following vulnerability has been resolved: x86/CPU/AMD: Add RDSEED fix for Zen5 There's an issue with RDSEED's 16-bit and 32-bit register output variants on Zen5 which return a random value of 0 "at a rate inconsistent with randomness while incorrectly signaling success...