5328 matches found
Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection
Credit card fraud detection CCFD is a critical application of Machine Learning ML in the financial sector, where accurately identifying fraudulent transactions is essential for mitigating financial losses. ML models have demonstrated their effectiveness in fraud detection task, in particular with...
Aura-CAPTCHA: a Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System
Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition OCR and adversarial image processing. The design integrated Generative Adversarial Networks GANs fo...
Linksys多款产品 安全漏洞
Linksys RE6300 and others are products of Linksys, Inc.Linksys RE6300 is a wireless network signal extender.Linksys RE6250 is a wireless extender.Linksys RE6350 is a wireless extender.Linksys RE6350 is a wireless extender.Linksys RE6350 is a wireless extender.Linksys RE6350 is a wireless...
CVE-2025-50461
A deserialization vulnerability exists in Volcengine's verl 3.0.0, specifically in the scripts/modelmerger.py script when using the "fsdp" backend. The script calls torch.load with weightsonly=False on user-supplied .pt files, allowing attackers to execute arbitrary code if a maliciously crafted...
Enhancing Targeted Adversarial Attacks on Large Vision-Language Models through Intermediate Projector Guidance
Targeted adversarial attacks are essential for proactively identifying security flaws in Vision-Language Models before real-world deployment. However, current methods perturb images to maximize global similarity with the target text or reference image at the encoder level, collapsing rich visual...
CCFC: Core and Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection
Jailbreak attacks pose a serious challenge to the safe deployment of large language models LLMs. We introduce CCFC Core & Core-Full-Core, a dual-track, prompt-level defense framework designed to mitigate LLMs' vulnerabilities from prompt injection and structure-aware jailbreak attacks. CCFC...
CVE-2025-50461
A deserialization vulnerability exists in Volcengine's verl version 3.0.0 (specifically affected versions
VerilogLAVD: LLM-Aided Rule Generation for Vulnerability Detection in Verilog
Timely detection of hardware vulnerabilities during the early design stage is critical for reducing remediation costs. Existing early detection techniques often require specialized security expertise, limiting their usability. Recent efforts have explored the use of large language models LLMs for...
Schneider Electric多款产品 输入验证错误漏洞
The Schneider Electric Modicon M340 is a mid-range PLC programmable logic controller for industrial processes and infrastructure from Schneider Electric France. An input validation error vulnerability exists in various Schneider Electric products, which stems from improper input validation and...
Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
The advancement of AI technologies, particularly Large Language Models LLMs, has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs ULLMs as backends for malicious services. Understanding the...
MAJIC: Markovian Adaptive Jailbreaking Via Iterative Composition of Diverse Innovative Strategies
Large Language Models LLMs have exhibited remarkable capabilities but remain vulnerable to jailbreaking attacks, which can elicit harmful content from the models by manipulating the input prompts. Existing black-box jailbreaking techniques primarily rely on static prompts crafted with a single,...
Mitigating Jailbreaks with Intent-Aware LLMs
Despite extensive safety-tuning, large language models LLMs remain vulnerable to jailbreak attacks via adversarially crafted instructions, reflecting a persistent trade-off between safety and task performance. In this work, we propose Intent-FT, a simple and lightweight fine-tuning approach that...
Unlearning at Scale: Implementing the Right to Be Forgotten in Large Language Models
We study the right to be forgotten GDPR Art. 17 for large language models and frame unlearning as a reproducible systems problem. Our approach treats training as a deterministic program and logs a minimal per-microbatch record ordered ID hash, RNG seed, learning-rate value, optimizer-step counter...
CryptoScope: Utilizing Large Language Models for Automated Cryptographic Logic Vulnerability Detection
Cryptographic algorithms are fundamental to modern security, yet their implementations frequently harbor subtle logic flaws that are hard to detect. We introduce CryptoScope, a novel framework for automated cryptographic vulnerability detection powered by Large Language Models LLMs. CryptoScope...
Ollama <= 0.3.3 DoS
The version of Ollama installed on the remote host is prior or equal to 0.3.3. It is, therefore, affected by a vulnerability. A divide by zero vulnerability exists in ollama/ollama version v0.3.3. The vulnerability occurs when importing GGUF models with a crafted type for blockcount in the...
Malicious code in humanapi-models (npm)
The package humanapi-models was found to contain malicious code...
Malicious code in inventory-models (npm)
The package inventory-models was found to contain malicious code...
Malicious code in gp-db-models (npm)
The package gp-db-models was found to contain malicious code...