CVE-2026-39349

CVE-2026-39349 affects OrangeHRM Open Source versions 5.0 through 5.8, where certain sensitive fields were encrypted with AES in ECB mode, preserving block patterns and enabling potential pattern disclosure in stored data. The issue is fixed in 5.8.1. Details confirmed by the provided description...

PT-2026-30996

The Semtech LR11xx LoRa transceivers implement secure boot functionality using digital signatures to authenticate firmware. However, the implementation uses a non-standard cryptographic hashing algorithm that is vulnerable to second preimage attacks. An attacker with physical access to the device...

JWCrypto security vulnerabilities

JWCrypto is an open-source implementation of a JavaScript Object Signature and Encryption JOSE web standard. Versions of JWCrypto prior to 1.5.7 contained security vulnerabilities, which stemmed from insufficient validation of the compressed output size. This could allow unauthenticated attackers...

OrangeHRM encryption issue vulnerabilities

OrangeHRM is a human resources management system developed by the American company OrangeHRM. This system supports functions such as personnel information management, leave management, attendance management, and recruitment management. Versions of OrangeHRM prior to 5.8 contained a security...

PT-2026-30972

OrangeHRM is a comprehensive human resource management HRM system. From 5.0 to 5.8, OrangeHRM Open Source encrypts certain sensitive fields with AES in ECB mode, which preserves block-aligned plaintext patterns in ciphertext and enables pattern disclosure against stored data. This vulnerability i...

MGASA-2026-0085 Updated polkit-122 packages fix security vulnerability

Denial of service via unbounded input processing through standard input. CVE-2026-4897...

Updated polkit-122 packages fix security vulnerability

Denial of service via unbounded input processing through standard input. CVE-2026-4897...

CVE-2026-34768

A flaw was found in Electron. On Windows, when an application is installed to a path containing spaces and configured to open at login, the executable path is written to the system's Run registry key without proper quoting. An attacker with write access to an ancestor directory can exploit this t...

CVE-2026-35042 fast-jwt accepts unknown `crit` header extensions (RFC 7515 §4.1.11 MUST violation)

fast-jwt provides fast JSON Web Token JWT implementation. In 6.1.0 and earlier, fast-jwt does not validate the crit Critical Header Parameter defined in RFC 7515 §4.1.11. When a JWS token contains a crit array listing extensions that fast-jwt does not understand, the library accepts the token...

DEBIAN-CVE-2026-34379

OpenEXR provides the specification and reference implementation of the EXR file format, an image storage format for the motion picture industry. From 3.2.0 to before 3.2.7, 3.3.9, and 3.4.9, a misaligned memory write vulnerability exists in LossyDctDecoderexecute in...

Qualcomm Chipsets security vulnerabilities

Qualcomm Chipsets are a series of chipset developed by Qualcomm Incorporated in the United States. There are security vulnerabilities in Qualcomm Chipsets, which stem from handling non-standard FILS discovery frames with oversized operations during initial scanning, potentially leading to sudden...

Tenda CH22 security vulnerabilities

The Tenda CH22 is a network device produced by the Chinese company Tenda. Version 1.0.0.1 of the Tenda CH22 contains a security vulnerability. This vulnerability stems from incorrect handling of the “standard” parameter, which may lead to a stack buffer overflow attack...

[SECURITY] Fedora 42 Update: python3.12-3.12.13-2.fc42

Python 3.12 is an accessible, high-level, dynamically typed, interpreted programming language, designed with an emphasis on code readability. It includes an extensive standard library, and has a vast ecosystem of third-party libraries. The python3.12 package provides the "python3.12" executable:...

CVE-2026-34760

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

PYSEC-2026-2299

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

PYSEC-2026-2299

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

CVE-2026-34760

Summary: CVE-2026-34760 concerns vLLM’s audio processing path via Librosa. From version 0.5.5 up to before 0.18.0, Librosa used numpy.mean for mono downmix (to_mono), while ITU-R BS.775-4 specifies a weighted downmix. This mismatch creates inconsistency between audio perceived by humans and audio...

CVE-2026-34760

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

CVE-2026-34760 vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...

CVE-2026-34760 vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing tomono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results...