VulReaD: Knowledge-Graph-Guided Software Vulnerability Reasoning and Detection

Software vulnerability detection SVD is a critical challenge in modern systems. Large language models LLMs offer natural-language explanations alongside predictions, but most work focuses on binary evaluation, and explanations often lack semantic consistency with Common Weakness Enumeration CWE...

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection

Feature selection FS remains essential for building accurate and interpretable detection models, particularly in high-dimensional malware datasets. Conventional FS methods such as Extra Trees, Variance Threshold, Tree-based models, Chi-Squared tests, ANOVA, Random Selection, and Sequential...

SAFuzz: Semantic-Guided Adaptive Fuzzing for LLM-Generated Code

While AI-coding assistants accelerate software development, current testing frameworks struggle to keep pace with the resulting volume of AI-generated code. Traditional fuzzing techniques often allocate resources uniformly and lack semantic awareness of algorithmic vulnerability patterns, leading...

⚡ Weekly Recap: AI Skill Malware, 31Tbps DDoS, Notepad++ Hack, LLM Backdoors and More

Cyber threats are no longer coming from just malware or exploits. They’re showing up inside the tools, platforms, and ecosystems organizations use every day. As companies connect AI, cloud apps, developer tools, and communication systems, attackers are following those same paths. A clear pattern...

KRONE: Hierarchical and Modular Log Anomaly Detection

Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure is lost when they are stored as flat sequences. As a result, state-of-the-art methods risk missing true dependencies...

CVE-2026-22778 vLLM leaks a heap address when PIL throws an error

vLLM is an inference and serving engine for large language models LLMs. From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guess...

Turning threat reports into detection insights with AI

Security teams routinely need to transform unstructured threat knowledge, such as incident narratives, red team breach-path writeups, threat actor profiles, and public reports into concrete defensive action. The early stages of that work are often the slowest. These include extracting tactics,...

EUVD-2026-4711

vLLM vulnerable to Server-Side Request Forgery SSRF through MediaConnector...

CVE-2026-24779

vLLM is an inference and serving engine for large language models LLMs. Prior to version 0.14.1, a Server-Side Request Forgery SSRF vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The loadfromurl and loadfromurlasync methods obtain and process...

CVE-2026-24779 vLLM vulnerable to Server-Side Request Forgery (SSRF) in `MediaConnector`

vLLM is an inference and serving engine for large language models LLMs. Prior to version 0.14.1, a Server-Side Request Forgery SSRF vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The loadfromurl and loadfromurlasync methods obtain and process...

PT-2026-4853

A flaw has been found in Totolink A8000RU 7.1cu.643 b20200521. This issue affects the function setWiFiAclRules of the file /cgi-bin/cstecgi.cgi of the component CGI Handler. This manipulation of the argument mode causes os command injection. The attack is possible to be carried out remotely. The...

CVE-2026-22807

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. This vulnerability allows a remote attacker to achieve arbitrary code execution on the vLLM host during model loading. This occurs because vLLM loads Hugging Face automap dynamic modules without properly...

VoidLink Linux Malware Framework Built with AI Assistance Reaches 88,000 Lines of Code

The recently discovered sophisticated Linux malware framework known as VoidLink is assessed to have been developed by a single person with assistance from an artificial intelligence AI model. That's according to new findings from Check Point Research, which identified operational security blunder...

LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation

In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large Language Models LLMs. By systematically applying carefully engineered prompts, we demonstrate how latent model behaviors can be influenced in...

Holmes: An Evidence-Grounded LLM Agent for Auditable DDoS Investigation in Cloud Networks

Cloud environments face frequent DDoS threats due to centralized resources and broad attack surfaces. Modern cloud-native DDoS attacks further evolve rapidly and often blend multi-vector strategies, creating an operational dilemma: defenders need wire-speed monitoring while also requiring...

VulnResolver: A Hybrid Agent Framework for LLM-Based Automated Vulnerability Issue Resolution

As software systems grow in complexity, security vulnerabilities have become increasingly prevalent, posing serious risks and economic costs. Although automated detection tools such as fuzzers have advanced considerably, effective resolution still often depends on human expertise. Existing...

AJAR: Adaptive Jailbreak Architecture for Red-Teaming

As Large Language Models LLMs evolve from static chatbots into autonomous agents capable of tool execution, the landscape of AI safety is shifting from content moderation to action security. However, existing red-teaming frameworks remain bifurcated: they either focus on rigid, script-based text...

EUVD-2026-2708

SparkyFitness v0.15.8.2 is vulnerable to Cross Site Scripting XSS via user input and LLM output...

CVE-2025-65368

SparkyFitness v0.15.8.2 is vulnerable to Cross Site Scripting XSS via user input and LLM output...

SparkyFitness security vulnerability

SparkyFitness is a fitness and health management platform developed by CodeWithCJ. Version SparkyFitness v0.15.8.2 contains a security vulnerability, which stems from improper handling of user input and LLM outputs, potentially leading to cross-site scripting attacks...