MAL-2025-4930 Malicious code in os-apps-ui-curvelibrary (npm)

--- -= Per source details. Do not edit below this line.=- Source: ossf-package-analysis 0119e0d30c93e83b68f00c9ab5d2f00f90d631cf3e692cab103e99c3ca6331b5 The OpenSSF Package Analysis project identified 'os-apps-ui-curvelibrary' @ 11.1.20 npm as malicious. It is considered malicious because: - The...

MAL-2025-4725 Malicious code in frontegg-nuxt-example (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 1dfeb24eb6c59e883dded7166ce9ff73fb43ab8352fcc2a154f86c7bf96be5e8 Any computer that has this package installed or running should be considered...

MAL-2025-4724 Malicious code in next-pwa-template (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 598361c7d39c208feedabd0f7d7e0b666d50ad75fa8f2c7db2a64654e3c6a194 Any computer that has this package installed or running should be considered...

IF-GUIDE: Influence Function-Guided Detoxification of LLMs

We study how training data contributes to the emergence of toxic behaviors in large-language models. Most prior work on reducing model toxicity adopts $reactive$ approaches, such as fine-tuning pre-trained and potentially toxic models to align them with human values. In contrast, we propose a...

HauntAttack: When Attack Follows Reasoning As a Shadow

Emerging Large Reasoning Models LRMs consistently excel in mathematical and reasoning tasks, showcasing exceptional capabilities. However, the enhancement of reasoning abilities and the exposure of their internal reasoning processes introduce new safety vulnerabilities. One intriguing concern is:...

MAL-2025-4738 Malicious code in db-prd (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware fdfa8c0490d93357820e77f9a51a08b6c15f03a8ee291c238c1960064b545e55 Any computer that has this package installed or running should be considered...

Malicious code in db-prd (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware fdfa8c0490d93357820e77f9a51a08b6c15f03a8ee291c238c1960064b545e55 Any computer that has this package installed or running should be considered...

MAL-2025-4744 Malicious code in gs-payments (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 83d0ff4617e5d8536d36cf0582895637c1549337a25ffacd854bf066b33ce9fb Any computer that has this package installed or running should be considered...

MAL-2025-4730 Malicious code in apple-api-fake (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware d4d50287073ec50503a3def9a3bd2b04b18b45b30a55b983b4e5f50ccc00b8a7 Any computer that has this package installed or running should be considered...

MAL-2025-4760 Malicious code in prod-shared (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 480110f6602e42420289e8da2e148e602ed3fc69063ba8c47edbcf8110c0c397 Any computer that has this package installed or running should be considered...

MAL-2025-4758 Malicious code in prd-utils (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=-...

MAL-2025-4708 Malicious code in www-cfg (npm)

--- -= Per source details. Do not edit below this line.=- Source: ossf-package-analysis 8e83ae0e09d965d0daf4532cb29c1b79698d342dc5afb338632d224d1f2706cc The OpenSSF Package Analysis project identified 'www-cfg' @ 1.0.2 npm as malicious. It is considered malicious because: - The package communicat...

MAL-2025-4707 Malicious code in virtru-private (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 60777031b508b2b27184e7bcdd9afb52ab3ca2e19bda0d7d4dee9333e7ff1190 Any computer that has this package installed or running should be considered...

MAL-2025-4756 Malicious code in pages-admin (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=-...

MAL-2025-4751 Malicious code in moonpay-demo-integrations (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=-...

MAL-2025-4669 Malicious code in world-id-poap (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ossf-package-analysis bdb64432a67fa7109c5ee4d1d5b94d0127eaedab876302eb3b246ae55b111498 The OpenSSF Package Analysis project identified 'world-id-poap' @ 1.0...

MAL-2025-4757 Malicious code in pizza-delivery (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 32883280f27dee6f08c25f84fa750e17fad3b3193488b14da6a77cddf52ef582 Any computer that has this package installed or running should be considered...

Hello, Won'T You Tell Me Your Name?: Investigating Anonymity Abuse in IPFS

The InterPlanetary File SystemIPFS offers a decentralized approach to file storage and sharing, promising resilience and efficiency while also realizing the Web3 paradigm. Simultaneously, the offered anonymity raises significant questions about potential misuse. In this study, we explore methods...

MAL-2025-4768 Malicious code in test_for-pentest (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=-...

ReGA: Representation-Guided Abstraction for Model-Based Safeguarding of LLMs

Large Language Models LLMs have achieved significant success in various tasks, yet concerns about their safety and security have emerged. In particular, they pose risks in generating harmful content and vulnerability to jailbreaking attacks. To analyze and monitor machine learning models,...