EUVD-2025-81898

Malicious code in finewallabydumbs npm...

Malicious code in fine_mosquito_z3n (npm)

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EUVD-2025-70405

Malicious code in finemosquitoz3n npm...

EUVD-2025-64034

Malicious code in finetapirz3n npm...

EUVD-2025-64038

Malicious code in fineconstrictorrequirement npm...

EUVD-2025-64036

Malicious code in finemarmosetz3n npm...

Malicious code in fine_tapir_z3n (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 3fdc65840c5e25597b49ad169a5ae7de66458d71c3e5cf3101d6904c52daa3b8 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-2025-64039

Malicious code in fineamphibianz3n npm...

Malicious code in fine_mongoose_z3n (npm)

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EUVD-2025-54320

Malicious code in fine-amaranth-hedgehog npm...

EUVD-2025-54318

Malicious code in fine-gold-worm npm...

EUVD-2025-54316

Malicious code in fine-peach-cockroach 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)

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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...