35640 matches found
Compromised IAM Credentials Power a Large AWS Crypto Mining Campaign
An ongoing campaign has been observed targeting Amazon Web Services AWS customers using compromised Identity and Access Management IAM credentials to enable cryptocurrency mining. The activity, first detected by Amazon's GuardDuty managed threat detection service and its automated security...
CVE-2025-68315
In the Linux kernel, the following vulnerability has been resolved: f2fs: fix to detect potential corrupted nid in freenidlist As reported, on-disk footer.ino and footer.nid is the same and out-of-range, let's add sanity check on f2fsallocnid to detect any potential corruption in freenidlist...
UBUNTU-CVE-2025-68219
In the Linux kernel, the following vulnerability has been resolved: cifs: fix memory leak in smb3fscontextparseparam error path Add proper cleanup of ctx-source and fc-source to the cifsparsemounterr error handler. This ensures that memory allocated for the source strings is correctly freed on al...
Exploit for Path Traversal in Rarlab Winrar
!Window...
CIS-BA: Continuous Interaction Space Based Backdoor Attack for Object Detection in the Real-World
Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their practical effectiveness,existing methods are inherently limited in both capability and robustness due to their dependence on single-trigger-single-objec...
UIXPOSE: Mobile Malware Detection Via Intention-Behaviour Discrepancy Analysis
We introduce UIXPOSE, a source-code-agnostic framework that operates on both compiled and open-source apps. This framework applies Intention Behaviour Alignment IBA to mobile malware analysis, aligning UI-inferred intent with runtime semantics. Previous work either infers intent statically, e.g.,...
Nuclei 3.6.1
Nuclei is a modern, high-performance vulnerability scanner that leverages simple YAML-based templates. It empowers you to design custom vulnerability detection scenarios that mimic real-world conditions, leading to zero false positives...
Intrusion Detection in Internet of Vehicles Using Machine Learning
The Internet of Vehicles IoV has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptible to cyber-attacks such Denial-ofService DoS and message spoofing. This project...
ScamSweeper: Detecting Illegal Accounts in Web3 Scams Via Transactions Analysis
The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated...
PT-2025-51640
Name of the Vulnerable Software and Affected Versions Linux kernel affected versions not specified Description A flaw exists in the Linux kernel’s MPTCP implementation related to protocol fallback detection with BPF. The issue arises when a server has MPTCP enabled, but a client sends a TCP SYN...
Linux kernel 安全漏洞
Linux kernel is the kernel used by Linux, the open source operating system of the Linux Foundation in the United States. A security vulnerability exists in the Linux kernel that stems from improper serial device detection and could lead to null pointer dereferencing...
Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models
Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital...
Behavior-Aware and Generalizable Defense against Black-Box Adversarial Attacks for ML-Based IDS
Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals like response time and resource usage. While several defenses have been proposed...
Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection
Network Intrusion Detection Systems NIDS are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. Th...
FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection
Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated ...
Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring
This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity...
Taint-Based Code Slicing for LLMs-Based Malicious NPM Package Detection
The increasing sophistication of malware attacks in the npm ecosystem, characterized by obfuscation and complex logic, necessitates advanced detection methods. Recently, researchers have turned their attention from traditional detection approaches to Large Language Models LLMs due to their strong...
Diverse LLMs Vs. Vulnerabilities: Who Detects and Fixes Them Better?
Large Language Models LLMs are increasingly being studied for Software Vulnerability Detection SVD and Repair SVR. Individual LLMs have demonstrated code understanding abilities, but they frequently struggle when identifying complex vulnerabilities and generating fixes. This study presents...
