279 matches found
CVE-2026-41940
CVE-2026-41940 ✅ Script analysis what the scanner does and how ✅ Root cause technical explanation the CRLF/Session.pm diff ✅ Prevention — patch commands, secure coding lessons ✅ Mitigation — firewall rules, WAF config, hardening ✅ IOC hunting — detecting if you were already hit ✅ VaPT methodology...
CVE-2021-44228---Log4Shell-Analysis
🔥 Log4Shell CVE-2021-44228 Analysis A comprehensive technical analysis of the Log4Shell vulnerability CVE-2021-44228, one of the most critical vulnerabilities in modern software history. 📖 Overview This repository contains an in-depth analysis of the Log4Shell vulnerability in Apache Log4j2,...
Follina-Vulnerability-CVE-2022-30190-Exploit-Analysis
Follina-Vulnerability-CVE-2022-30190-Exploit-Analysis Exploration of the Follina CVE-2022-30190 Microsoft Office vulnerability, including a detailed analysis, proof-of-concept exploitation in a controlled lab, and mitigation strategies. For educational and research purposes only...
From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applications
Large Language Models are increasingly integrated into web applications through chatbots, tool-calling pipelines, and agentic workflows. In these systems, user input may influence not only generated text, but also backend actions such as database queries, HTTP requests, file operations, template...
Delay Attacks on the German Smart Metering Infrastructure: A Security Analysis of CLS Channel Timing Constraints
This work analyzes the feasibility of delay attacks on control signals transmitted via the Controllable Local System CLS channel of the German Smart Metering Infrastructure SMI. It combines theoretical analysis with experimental validation under a threat model aligned to the Common Criteria...
Exploit for Observable Discrepancy in Intel Atom_C
🛡️ Spectre Variant 2 CVE-2017-5715 — Hardware Vulnerability...
An Empirical Study of Security Calibration in Large Language Models for Code
Large Language Models LLMs are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure? This property, known as calibration, measures whether a model's confidence aligns with the true...
LLM Agents Security Duality: A Comprehensive Survey of Self-Security and Empowered Cybersecurity
Large language model LLM agents are rapidly being integrated into real-world systems. Their autonomy and tool-use capabilities generate substantial value while simultaneously expanding the security attack surface. This survey provides a comprehensive overview of the opportunities and challenges o...
wannacry-soc-lab
WannaCry SOC Investigation Lab Overview This project simu...
Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications
Artificial Intelligence AI is increasingly used to automate a variety of real-world computer vision CV applications, such as autonomous vehicle control, facial recognition, and security cameras. Recent research has shown that acoustic vibration can induce real physical motion in cameras,...
Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems
The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilitie...
When Discovery Outpaces Remediation: Modeling AI-Accelerated Vulnerability Discovery in Interconnected Systems
Advanced AI systems for code analysis, binary analysis, fuzzing orchestration, and penetration-test planningmay significantly increase the rate at which latent vulnerabilities are discovered. While improved discovery can benefit defenders, it can also overload remediation pipelines and accelerate...
Unveiling Privacy Risks in Multi-Modal Large Language Models: Task-Specific Vulnerabilities and Mitigation Challenges
Privacy risks in text-only Large Language Models LLMs are well studied, particularly their tendency to memorize and leak sensitive information. However, Multi-modal Large Language Models MLLMs, which process both text and images, introduce unique privacy challenges that remain underexplored...
Credential Disclosure in (EU) Digital Identity Wallets: Privacy Risks and Practical Mitigations
The European Union will introduce the EUDI Wallet by late 2026, which allows users to hold digital credentials i.e., representations of physical official identity documents on their devices. This will allow users to securely and privately disclose identity attributes to websites. Although such a...
Description-Code Inconsistency in Real-World MCP Servers: Measurement, Detection, and Security Implications
The Model Context Protocol MCP has emerged as a critical standard empowering Large Language Models LLMs to utilize external tools. In this ecosystem, LLMs rely on natural language descriptions provided by MCP servers to select and execute functions. This interaction implicitly assumes that tool...
Security Analysis of a Communication Protocol: MQTT
This paper analyzes the security of the Message Queuing Telemetry Transport MQTT protocol in the context of the Internet of Things IoT. The main objective consists of identifying vulnerabilities and proposing security improvements. Adopting a hybrid methodology, a theoretical review was combined...
Security Risks in Tool-Enabled AI Agents: A Systematic Analysis of Privileged Execution Environments
Tool-enabled AI agents are increasingly deployed in cloud-hosted environments and offered as services, where they perform side-effecting operations through privileged tools within execution environments. While such agents enable powerful automation, the security implications of hosting autonomous...
Memory Poisoning and Secure Multi-Agent Systems
Memory poisoning attacks for Agentic AI and multi-agent systems MAS have recently caught attention. It is partially due to the fact that Large Language Models LLMs facilitate the construction and deployment of agents. Different memory systems are being used nowadays in this context, including...
Threat modeling AI applications
Proactively identifying, assessing, and addressing risk in AI systems We cannot anticipate every misuse or emergent behavior in AI systems. We can , however, identify what can go wrong, assess how bad it could be, and design systems that help reduce the likelihood or impact of those failure modes...