CVE-2012-1430

The ELF file parser in Bitdefender 7.2, Comodo Antivirus 7424, eSafe 7.0.17.0, F-Secure Anti-Virus 9.0.16160.0, McAfee Anti-Virus Scanning Engine 5.400.0.1158, McAfee Gateway formerly Webwasher 2010.1C, nProtect Anti-Virus 2011-01-17.01, Sophos Anti-Virus 4.61.0, and Rising Antivirus 22.83.00.03...

CVE-2010-5179

Race condition in Trend Micro Internet Security Pro 2010 17.50.1647.0000 on Windows XP allows local users to bypass kernel-mode hook handlers, and execute dangerous code that would otherwise be blocked by a handler but not blocked by signature-based malware detection, via certain user-space memor...

CVE-2012-1441

The Microsoft EXE file parser in eSafe 7.0.17.0 and Prevx 3.0 allows remote attackers to bypass malware detection via an EXE file with a modified value in any of several e fields. NOTE: this may later be SPLIT into multiple CVEs if additional information is published showing that the error occurr...

CVE-2012-1436

The Microsoft EXE file parser in AhnLab V3 Internet Security 2011.01.18.00, Emsisoft Anti-Malware 5.1.0.1, eSafe 7.0.17.0, Ikarus Virus Utilities T3 Command Line Scanner 1.1.97.0, and Panda Antivirus 10.0.2.7 allows remote attackers to bypass malware detection via an EXE file with a \2D\6C\68...

CVE-2009-5125

Comodo Internet Security before 3.9.95478.509 allows remote attackers to bypass malware detection in an RAR archive via an unspecified manipulation of the archive file format...

Sophisticated & Stealthy Formjacking Malware Targets E-Commerce Checkout Pages

📢In case you missed it, Wordfence just published itsannual WordPress security report for 2024. Read it now to learn more about the evolving risk landscape of WordPress so you can keep your sites protected in 2025 and beyond. The Wordfence Threat Intelligence team recently uncovered a sophisticate...

MAL-2025-4136 Malicious code in typescri (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 38f04c3fc11a28f8c6d0deae7aa58501ba6e77e2641fac6e541e6ced55c8928c Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

MAL-2025-4105 Malicious code in modules-dmall-discord.js (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 3599d04fe3ad9f41d62d1da7a44146ad6a523119af78f3a1b496a53bd8da6c61 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

CVE-2025-47939

TYPO3 CMS vulnerability CVE-2025-47939 affects TYPO3 versions prior to 9.5.51 ELTS, 10.4.50 ELTS, 11.5.44 ELTS, 12.4.31 LTS, and 13.4.12 LTS. The issue is an unrestricted file upload in the File Abstraction Layer: the file management backend allowed uploading any file type, including potentially ...

Malware-infected printer delivered something extra to Windows users

You'd hope that spending $6,000 on a printer would give you a secure experience, free from viruses and other malware. However, in the case of Procolored printers, you'd be wrong. The Shenzen-based company sells UV printers, which are able to print on a variety of materials including wood, acrylic...

Malware Families Discovery Via Open-Set Recognition on Android Manifest Permissions

Malware are malicious programs that are grouped into families based on their penetration technique, source code, and other characteristics. Classifying malware programs into their respective families is essential for building effective defenses against cyber threats. Machine learning models have ...

MAL-2025-3935 Malicious code in eslint-plugin-i18n-strings (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 52f5d25719716952625ab6fabacd4ccb2743e7066584b9b76cf5c198a0ebfc66 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems

Machine learning is a key tool for Android malware detection, effectively identifying malicious patterns in apps. However, ML-based detectors are vulnerable to evasion attacks, where small, crafted changes bypass detection. Despite progress in adversarial defenses, the lack of comprehensive...

Dual Explanations Via Subgraph Matching for Malware Detection

Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Networks GNNs often highlight important regions within a graph but fail to associate them with known benign or maliciou...

MAL-2025-6609 Malicious code in tronlid (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 387ea56c726485890b55cce5a96c6381e248be3c8eba22c22ead08e4b30db3b1 Package appears to be designed for private key exfiltration, but no known usage. The name appears to be related to the cryptocurrency TRX Tron / Tronix. Some...

MAL-2025-4227 Malicious code in ora3 (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 9d9ff95457d63990b263b77b0f3468dcd63e0d1837c81843b2d4249706db48c7 Package contains just a function to send out data. It or a package sharing the same IoCs is used in a malicious GitHub project to exfiltrate crypto currency...

Optimized Approaches to Malware Detection: a Study of Machine Learning and Deep Learning Techniques

Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to operate properly and yield high false positive rates with l...

On the Consistency of GNN Explanations for Malware Detection

Control Flow Graphs CFGs are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks GNNs, CFG-based representations have proven highly effective for malware detection. This study proposes a novel framework that dynamically...

Zero Day Malware Detection with Alpha: Fast DBI with Transformer Models for Real World Application

The effectiveness of an AI model in accurately classifying novel malware hinges on the quality of the features it is trained on, which in turn depends on the effectiveness of the analysis tool used. Peekaboo, a Dynamic Binary Instrumentation DBI tool, defeats malware evasion techniques to capture...

MAL-2025-3240 Malicious code in fatfingers-hahayeet (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware ab10b9a2ff027ef8bfac4c8953e64c6a24e799afecb082e727da93d58fd0cb47 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...