CVE-2026-5673

CVE-2026-5673 affects libtheora’s AVI parser. The flaw is a heap-based out-of-bounds read in the avi_parse_input_file() function triggered by a crafted AVI file with a truncated header sub-chunk. Local attackers can exploit this by tricking a user into opening such a file, leading to a potential ...

CVE-2026-5673

A flaw was found in libtheora. This heap-based out-of-bounds read vulnerability exists within the AVI Audio Video Interleave parser, specifically in the aviparseinputfile function. A local attacker could exploit this by tricking a user into opening a specially crafted AVI file containing a...

kernel: ALSA: aloop: Fix racy access at PCM trigger

In the Linux kernel, the following vulnerability has been resolved: ALSA: aloop: Fix racy access at PCM trigger The PCM trigger callback of aloop driver tries to check the PCM state and stop the stream of the tied substream in the corresponding cable. Since both check and stop operations are...

Linux Distros Unpatched Vulnerability : CVE-2026-5673

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - A flaw was found in libtheora. This heap-based out-of-bounds read vulnerability exists within the AVI Audio Video Interleave parser, specifically in the...

Perceptual Gaps: ASCII Art and Overlapping Audio As CAPTCHA

As multimodal large language models LLMs advance, traditional CAPTCHAs have become obsolete at distinguishing humans from bots. To address this shift, this paper aims to investigate the possibility of using tasks for which humans have evolved highly specialised neural processing. We introduce two...

[SECURITY] Fedora 42 Update: gst-editing-services-1.26.11-1.fc42

This is a high-level library for facilitating the creation of audio/video non-linear editors...

Allocation of Resources Without Limits or Throttling

Overview openclaw is a 🦞 OpenClaw — Personal AI Assistant Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling in the preflight process for Telegram audio transcription. An attacker can cause excessive resource or billing consumption by triggeri...

OpenClaw: Telegram audio preflight transcription enables resource consumption by unauthorized senders

Summary Telegram audio preflight transcription enables resource consumption by unauthorized senders Current Maintainer Triage - Status: narrow - Normalized severity: medium - Assessment: v2026.3.28 still lets unauthorized Telegram group senders trigger audio preflight before allowlist enforcement...

Allocation of Resources Without Limits or Throttling

Overview @openclaw/discord is an OpenClaw Discord channel plugin Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the Discord audio preflight transcription process occurring before member authorization. An attacker can cause excessive...

OpenClaw runs Discord audio preflight transcription before member authorization

Summary Discord audio preflight transcription before member authorization Current Maintainer Triage - Status: narrow - Normalized severity: medium - Assessment: v2026.3.28 still runs Discord audio preflight before member allowlist rejection, but this is the same pre-auth resource-consumption clas...

Allocation of Resources Without Limits or Throttling

Overview openclaw is a 🦞 OpenClaw — Personal AI Assistant Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the Discord audio preflight transcription process occurring before member authorization. An attacker can cause excessive resour...

Improper Input Validation

Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Improper Input Validation due to inconsistent downmixing behavior in the tomono process. An attacker can manipulate audio inputs to cause the AI mod...

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

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...

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

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...