Vulnerabilities (CVE)

BullWall Ransomware Containment may not always detect an encrypted file. This issue affects a specific file inspection method that evaluates file content based on header bytes. An authenticated attacker could encrypt files, preserving the first four bytes and preventing this particular method from triggering. The affected product implements additional integrity-based detection mechanisms capable of identifying file corruption or encryption for some common file extensions independent of header by ... BullWall Ransomware Containment may not always detect an encrypted file. This issue affects a specific file inspection method that evaluates file content based on header bytes. An authenticated attacker could encrypt files, preserving the first four bytes and preventing this particular method from triggering. The affected product implements additional integrity-based detection mechanisms capable of identifying file corruption or encryption for some common file extensions independent of header bytes. As a result, this vulnerability does not represent a complete bypass of ransomware detection, but a limitation of one detection method when evaluated independently. Versions 4.6.0.0, 4.6.0.6, 4.6.0.7, and 4.6.1.4 are affected. Other versions may also be affected. BullWall plans to improve detection method documentation.

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Incomplete comparison with missing factors in Windows BitLocker allows an unauthorized attacker to bypass a security feature with a physical attack.

An issue was discovered in GitLab CE/EE affecting all versions prior to 16.11.6, starting from 17.0 prior to 17.0.4, and starting from 17.1 prior to 17.1.2, which allows a subdomain takeover in GitLab Pages.

vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30 ... vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0.

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An Incomplete Comparison with Missing Factors vulnerability in the Gallagher Controller allows an attacker to bypass PIV verification. This issue affects: Gallagher Command Centre 8.40 versions prior to 8.40.1888 (MR3); 8.30 versions prior to 8.30.1359 (MR3); 8.20 versions prior to 8.20.1259 (MR5); 8.10 versions prior to 8.10.1284 (MR7); version 8.00 and prior versions.