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Introducing Share Consumer Support (Kafka Queues) in Spring for Apache Kafka
Continuing our Road to GA series, this week we're exploring Share Groups in Apache Kafka 4.0.0 and their integration in Spring for Apache Kafka 4.0.0 - a feature that fundamentally expands how we can consume messages from Kafka topics. When we first start working with Kafka, the mental model is...
CTIArena: Benchmarking LLM Knowledge and Reasoning across Heterogeneous Cyber Threat Intelligence
Cyber threat intelligence CTI is central to modern cybersecurity, providing critical insights for detecting and mitigating evolving threats. With the natural language understanding and reasoning capabilities of large language models LLMs, there is increasing interest in applying them to CTI, whic...
A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges
The Tor network provides users with strong anonymity by routing their internet traffic through multiple relays. While Tor encrypts traffic and hides IP addresses, it remains vulnerable to traffic analysis attacks such as the website fingerprinting WF attack, achieving increasingly high...
(Dis)Proving Spectre Security with Speculation-Passing Style
Constant-time CT verification tools are commonly used for detecting potential side-channel vulnerabilities in cryptographic libraries. Recently, a new class of tools, called speculative constant-time SCT tools, has also been used for detecting potential Spectre vulnerabilities. In many cases, the...
Lightweight CNN-Based Wi-Fi Intrusion Detection Using 2D Traffic Representations
Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread adoption, create opportunities for malicious actors to gain...
DITTO: A Spoofing Attack Framework on Watermarked LLMs Via Knowledge Distillation
The promise of LLM watermarking rests on a core assumption that a specific watermark proves authorship by a specific model. We demonstrate that this assumption is dangerously flawed. We introduce the threat of watermark spoofing, a sophisticated attack that allows a malicious model to generate te...
SASER: Stego Attacks on Open-Source LLMs
Open-source large language models LLMs have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks to the collaborative and sharing nature. Although full access to source codes, model parameters, and training data lays the groundwork for transparenc...
ArtPerception: ASCII Art-Based Jailbreak on LLMs with Recognition Pre-Test
The integration of Large Language Models LLMs into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data...
A Systematic Study on Generating Web Vulnerability Proof-Of-Concepts Using Large Language Models
Recent advances in Large Language Models LLMs have brought remarkable progress in code understanding and reasoning, creating new opportunities and raising new concerns for software security. Among many downstream tasks, generating Proof-of-Concept PoC exploits plays a central role in vulnerabilit...
EUVD-2025-33778
Cherry Studio is a desktop client that supports for multiple LLM providers. Cherry Studio registers a custom protocol called cherrystudio://. When handling the MCP installation URL, it parses the base64-encoded configuration data and directly executes the command within it. In the files...
The AI SOC Stack of 2026: What Sets Top-Tier Platforms Apart?
The SOC of 2026 will no longer be a human-only battlefield. As organizations scale and threats evolve in sophistication and velocity, a new generation of AI-powered agents is reshaping how Security Operations Centers SOCs detect, respond, and adapt. But not all AI SOC platforms are created equal...
Pattern Enhanced Multi-Turn Jailbreaking: Exploiting Structural Vulnerabilities in Large Language Models
Large language models LLMs remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints gradually. These attacks target different harm categories like malware generation, harassment, or fraud through distinct conversational approaches...
RedTWIZ: Diverse LLM Red Teaming Via Adaptive Attack Planning
This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models LLMs in AI-assisted software development. Our work is driven by three major research streams: 1...
Distilling Lightweight Language Models for C/C++ Vulnerabilities
The increasing complexity of modern software systems exacerbates the prevalence of security vulnerabilities, posing risks of severe breaches and substantial economic loss. Consequently, robust code vulnerability detection is essential for software security. While Large Language Models LLMs have...
EUVD-2019-0039
Malware in sbrugna...
EUVD-2020-29203
Malware in sbrugna...
EUVD-2015-9062
Malware in sbrugna...
EUVD-2008-3804
Malware in sbrugna...