549 matches found
Malicious code in fine_mongoose_z3n (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector dcfd074384fd8ad729db609c986095f73c0ecd91699ec30d4250421b7fcefad5 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-2025-54316
Malicious code in fine-peach-cockroach npm...
EUVD-2025-54320
Malicious code in fine-amaranth-hedgehog npm...
EUVD-2025-54318
Malicious code in fine-gold-worm npm...
EUVD-2025-54319
Malicious code in fine-black-silkworm npm...
Malicious code in fine-amaranth-hedgehog (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector ea008d503031c33581c21b98018cc662d33bba2c9e28a3ac52e1980e17a22648 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
Malicious code in fine-gold-worm (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 5e4bf0df8b3d184a316d02d88836a4d273c9de8420a48cf1a5b19aee17c35b92 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-2025-54317
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: Improper Access Control
ABC Fine Wine & Spirits Android App version v.11.27.5 and before package name com.cta.abcfinewineandspirits, developed by ABC Liquors, Inc., contains an improper access control vulnerability in its login mechanism. The application does not properly validate user passwords during authentication,...
CVE-2025-61115
The ABC Fine Wine & Spirits Android App (package name com.cta.abcfinewineandspirits), developed by ABC Liquors, Inc., is vulnerable to an improper access control flaw in its login mechanism affecting version v.11.27.5 and before . The root cause is a failure to properly validate user passwords du...
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...