Code Vulnerability Detection across Different Programming Languages with AI Models

Security vulnerabilities present in a code that has been written in diverse programming languages are among the most critical yet complicated aspects of source code to detect. Static analysis tools based on rule-based patterns usually do not work well at detecting the context-dependent bugs and...

Crypto24 Ransomware Group Blends Legitimate Tools with Custom Malware for Stealth Attacks

Crypto24 is a ransomware group that stealthily blends legitimate tools with custom malware, using advanced evasion techniques to bypass security and EDR technologies...

A Transformer-Based Approach for DDoS Attack Detection in IoT Networks

DDoS attacks have become a major threat to the security of IoT devices and can cause severe damage to the network infrastructure. IoT devices suffer from the inherent problem of resource constraints and are therefore susceptible to such resource-exhausting attacks. Traditional methods for detecti...

Enhancing GraphQL Security by Detecting Malicious Queries Using Large Language Models, Sentence Transformers, and Convolutional Neural Networks

GraphQL's flexibility, while beneficial for efficient data fetching, introduces unique security vulnerabilities that traditional API security mechanisms often fail to address. Malicious GraphQL queries can exploit the language's dynamic nature, leading to denial-of-service attacks, data...

Can Multi-Modal (Reasoning) LLMs Detect Document Manipulation?

Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the efficacy of state-of-the-art multi-modal large language models LLMs-including OpenAI O1, OpenAI 4o, Gemini Flash thinking,...

Google Chrome Security Update (stable-channel-update-for-desktop_12-2025-08) - Windows

Google Chrome is prone to multiple vulnerabilities. SPDX-FileCopyrightText: 2025 Greenbone AG Some text descriptions might be excerpted from a referenced sources, and are Copyright C by the respective right holders. SPDX-License-Identifier: GPL-2.0-only CPE = "cpe:/a:google:chrome"; ifdescription...

swift-nio-http2 affected by HTTP/2 MadeYouReset vulnerability

The HTTP/2 MadeYouReset vulnerability has a mild effect on swift-nio-http2. swift-nio-http2 mostly protects against MadeYouReset by using a number of existing denial-of-service prevention patterns that we added in response to the RapidReset vulnerabilities. The result is that servers are not...

Exploit for Command Injection in Microsoft

💥 CVE-2025-53773 — Remote Code Execution in GitHub Copilot 💥...

CVE-2025-8939

creationtimestamp| type| source ---|---|--- 2025-08-13 12:55:48+00:00| seen| https://gist.github.com/lin-3-start/045d659e8c0c0779826f737b894adf85...

AI SOC 101: Key Capabilities Security Leaders Need to Know

Security operations have never been a 9-to-5 job. For SOC analysts, the day often starts and ends deep in a queue of alerts, chasing down what turns out to be false positives, or switching between half a dozen tools to piece together context. The work is repetitive, time-consuming, and high-stake...

Malicious File Parsing

@finos/git-proxy is vulnerable to malicious file parsing. The vulnerability is due to improper PACK signature detection in parsePush.ts, which allows an attacker to embed misleading signatures in commit content and craft packet structures to bypass approval or hide commits...

Dell Storage Manager Data Collector Detection

Binary data dellstoragemanagerdatacollectordetect.nbin...

Demystifying the Role of Rule-Based Detection in AI Systems for Windows Malware Detection

Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples,...

Causal Graph Profiling Via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems

With the growing complexity of cyberattacks targeting critical infrastructures such as water treatment networks, there is a pressing need for robust anomaly detection strategies that account for both system vulnerabilities and evolving attack patterns. Traditional methods -- statistical,...

Explainable Ensemble Learning for Graph-Based Malware Detection

Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks GNNs have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such a...

Ashlar-Vellum Cobalt, Xenon, Argon, Lithium, Cobalt Share

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to disclose information and execute arbitrary code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities, such as: Minimize...

GHSA-9GVJ-PP9X-GCFR Picklescan has pickle parsing logic flaw that leads to malicious pickle file bypass

Details There's a parsing logic error in picklescan and modelscan while trying to deal with opcode STACKGLOBAL. Function listglobals when handling STACKGLOBAL at position n, it is expected to track two arguments but in wrong range. The loop only consider the range from 1 to n-1 but forgets to...

Picklescan has pickle parsing logic flaw that leads to malicious pickle file bypass

Details There's a parsing logic error in picklescan and modelscan while trying to deal with opcode STACKGLOBAL. Function listglobals when handling STACKGLOBAL at position n, it is expected to track two arguments but in wrong range. The loop only consider the range from 1 to n-1 but forgets to...

New Ransomware Charon Uses Earth Baxia APT Techniques to Target Enterprises

We uncovered Charon, a new ransomware strainfamily that uses advanced APT-style techniques, including DLL sideloading, process injection, and anti-EDR capabilities, to target organizations with customized ransom demands...

MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregation

Face Morphing Attack Detection MAD is a critical challenge in face recognition security, where attackers can fool systems by interpolating the identity information of two or more individuals into a single face image, resulting in samples that can be verified as belonging to multiple identities by...