512 matches found
EUVD-2025-54317
Malicious code in fine-jade-peacock npm...
MAL-2025-68332 Malicious code in fine-jade-peacock (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 388eff429a134b5f1627b7a8a18cabeabbf0dbd951843798e1de3f01b7d31ba5 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
EUVD-2025-54315
Malicious code in fine-tomato-capybara npm...
On the Dangers of Poisoned LLMs in Security Automation
This paper investigates some of the risks introduced by "LLM poisoning," the intentional or unintentional introduction of malicious or biased data during model training. We demonstrate how a seemingly improved LLM, fine-tuned on a limited dataset, can introduce significant bias, to the extent tha...
Scam Shield: Multi-Model Voting and Fine-Tuned LLMs against Adversarial Attacks
Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical Scam Detection System HSDS that combines a lightweight multi-model voting front end with a fine-tuned LLaMA 3.1 8B Instruct back end to improve accura...
CVE-2025-61115
CVE-2025-61115 affects ABC Fine Wine & Spirits Android App versions v.11.27.5 and earlier (package com.cta.abcfinewineandspirits). The root cause is improper access control in the login mechanism: the app does not properly validate user passwords during authentication, allowing bypass of login ch...
ABC Fine Wine & Spirits Android App 安全漏洞
ABC Fine Wine & Spirits Android App is a wine shopping app by ABC Fine Wine & Spirits. A security vulnerability exists in ABC Fine Wine & Spirits Android App v.11.27.5 and earlier versions, which stems from improper access control of the login mechanism and could lead to bypassing login checks an...
Jailbreak Mimicry: Automated Discovery of Narrative-Based Jailbreaks for Large Language Models
Large language models LLMs remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety mechanisms, posing significant risks in cybersecurity applications. We introduce Jailbreak Mimicry, a systematic methodology for training compact attacker mode...
REx86: A Local Large Language Model for Assisting in X86 Assembly Reverse Engineering
Reverse engineering RE of x86 binaries is indispensable for malware and firmware analysis, but remains slow due to stripped metadata and adversarial obfuscation. Large Language Models LLMs offer potential for improving RE efficiency through automated comprehension and commenting, but cloud-hosted...
Bloodroot: When Watermarking Turns Poisonous for Stealthy Backdoor
Backdoor data poisoning is a crucial technique for ownership protection and defending against malicious attacks. Embedding hidden triggers in training data can manipulate model outputs, enabling provenance verification, and deterring unauthorized use. However, current audio backdoor methods are...
EUVD-2008-1816
Malware in sbrugna...
EUVD-2005-1498
Malware in sbrugna...
EUVD-2020-3634
Malware in sbrugna...
P2P: A Poison-To-Poison Remedy for Reliable Backdoor Defense in LLMs
During fine-tuning, large language models LLMs are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings...
EUVD-2024-20867
Malicious code in bioql PyPI...
EUVD-2024-19186
Malicious code in bioql PyPI...
EUVD-2021-7351
Malicious code in bioql PyPI...
EUVD-2024-2914
Malicious code in bioql PyPI...