PatchSeeker: Mapping NVD Records to Their Vulnerability-Fixing Commits with LLM Generated Commits and Embeddings

Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database NVD is the authoritative source for cataloging these vulnerabilities, it often lacks explicit links to the corresponding Vulnerability-Fixing Commits VFCs. VFCs encode precise code...

Backdoor Attacks and Defenses in Computer Vision Domain: a Survey

Backdoor trojan attacks embed hidden, controllable behaviors into machine-learning models so that models behave normally on benign inputs but produce attacker-chosen outputs when a trigger is present. This survey reviews the rapidly growing literature on backdoor attacks and defenses in the...

Signal-Based Malware Classification Using 1D CNNs

Malware classification is a contemporary and ongoing challenge in cyber-security: modern obfuscation techniques are able to evade traditional static analysis, while dynamic analysis is too resource intensive to be deployed at a large scale. One prominent line of research addresses these limitatio...

Contrastive Self-Supervised Network Intrusion Detection Using Augmented Negative Pairs

Network intrusion detection remains a critical challenge in cybersecurity. While supervised machine learning models achieve state-of-the-art performance, their reliance on large labelled datasets makes them impractical for many real-world applications. Anomaly detection methods, which train...

Decoding Latent Attack Surfaces in LLMs: Prompt Injection Via HTML in Web Summarization

Large Language Models LLMs are increasingly integrated into web-based systems for content summarization, yet their susceptibility to prompt injection attacks remains a pressing concern. In this study, we explore how non-visible HTML elements such as , aria-label, and alt attributes can be exploit...

Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models

Large Language Models LLMs are increasingly vulnerable to a sophisticated form of adversarial prompting known as camouflaged jailbreaking. This method embeds malicious intent within seemingly benign language to evade existing safety mechanisms. Unlike overt attacks, these subtle prompts exploit...

VulnRepairEval: an Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities

The adoption of Large Language Models LLMs for automated software vulnerability patching has shown promising outcomes on carefully curated evaluation sets. Nevertheless, existing datasets predominantly rely on superficial validation methods rather than exploit-based verification, leading to...

CVE-2025-57140

rsbi-pom 4.7 is vulnerable to SQL Injection in the /bi/service/model/DatasetService path...

Performance Analysis of Common Browser Extensions for Cryptojacking Detection

This paper considers five extensions for Chromium-based browsers in order to determine how effective can browser-based defenses against cryptojacking available to regular users be. We've examined most popular extensions - MinerBlock, AdGuard AdBlocker, Easy Redirect && Prevent Cryptojacking,...

An Intrusion Detection System in Internet of Things Using Grasshopper Optimization Algorithm and Machine Learning Algorithms

The Internet of Things IoT has emerged as a foundational paradigm supporting a range of applications, including healthcare, education, agriculture, smart homes, and, more recently, enterprise systems. However, significant advancements in IoT networks have been impeded by security vulnerabilities...

E-PhishGen: Unlocking Novel Research in Phishing Email Detection

Every day, our inboxes are flooded with unsolicited emails, ranging between annoying spam to more subtle phishing scams. Unfortunately, despite abundant prior efforts proposing solutions achieving near-perfect accuracy, the reality is that countering malicious emails still remains an unsolved...

AI Agentic Vulnerability Injection and Transformation with Optimized Reasoning

The increasing complexity of software systems and the sophistication of cyber-attacks have underscored the critical need for effective automated vulnerability detection and repair systems. Traditional methods, such as static program analysis, face significant challenges related to scalability,...

FALCON: Autonomous Cyber Threat Intelligence Mining with LLMs for IDS Rule Generation

Signature-based Intrusion Detection Systems IDS detect malicious activities by matching network or host activity against predefined rules. These rules are derived from extensive Cyber Threat Intelligence CTI, which includes attack signatures and behavioral patterns obtained through automated tool...

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned

In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a...

Malicious code in lyft-dataset-sdk (npm)

The package lyft-dataset-sdk was found to contain malicious code...

MAL-2025-21231 Malicious code in gces-dataset (npm)

The package gces-dataset was found to contain malicious code...

Malicious code in gces-dataset (npm)

The package gces-dataset was found to contain malicious code...

MAL-2025-25712 Malicious code in lyft-dataset-sdk (npm)

The package lyft-dataset-sdk was found to contain malicious code...

BERTector: Intrusion Detection Based on Joint-Dataset Learning

Intrusion detection systems IDS are facing challenges in generalization and robustness due to the heterogeneity of network traffic and the diversity of attack patterns. To address this issue, we propose a new joint-dataset training paradigm for IDS and propose a scalable BERTector framework based...

Enhance the Machine Learning Algorithm Performance in Phishing Detection with Keyword Features

Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive...