CVE-2026-61722
Linux Debian vulnerability analysis and mitigation

Overview

CVE-2026-61722 is a DLS Articulation Chunk Integer Overflow vulnerability in FluidSynth's native DLS parser, affecting versions 2.5.0 through 2.5.5. The vulnerability was published on July 4, 2026, by the FluidSynth project maintainer, with a patched version (2.5.6) released concurrently. It carries a CVSS v3.1 base score of 6.8 (Moderate) (GitHub Advisory). Debian Linux's fluidsynth package is noted as containing this unpatched vulnerability with no vendor-supplied patch available at the time of Feedly's tracking (OSV).

Technical details

The root cause is an integer overflow (CWE-190) in FluidSynth's native DLS parser. The expression cbsize + connblocks * 12 overflows when connblocks equals 0x40000001, causing the loop to read 12 bytes per iteration for approximately one billion iterations — totaling roughly 12.8 GB of data read past the chunk boundary. Exploitation requires a locally supplied malicious DLS file; no network access or elevated privileges are needed. The first erroneous commit is identified as c959f8d, and the fix was applied in commit 4d7084f (GitHub Advisory).

Impact

Successful exploitation of this vulnerability primarily impacts availability (High) and, to a lesser extent, integrity (Low), with no confidentiality impact. An attacker who can supply a crafted DLS file to a vulnerable FluidSynth instance can trigger the integer overflow, causing the application to read approximately 12.8 GB past the chunk boundary, likely resulting in a crash or denial of service. Integrity impact is limited to potential minor data corruption from the out-of-bounds read behavior (GitHub Advisory).

Exploitation steps

  1. Craft a malicious DLS file: Create a DLS (Downloadable Sounds) file with a DLS Articulation Chunk where the connblocks field is set to 0x40000001, causing the expression cbsize + connblocks * 12 to overflow when parsed.
  2. Deliver the file to the target: Supply the crafted DLS file to an application using a vulnerable FluidSynth instance (versions 2.5.0–2.5.5) compiled with the enable-native-dls CMake option enabled (the default).
  3. Trigger parsing: Cause the application to load or process the malicious DLS file, invoking FluidSynth's native DLS parser.
  4. Integer overflow triggers out-of-bounds read: The parser overflows the integer calculation and enters a loop reading 12 bytes per iteration for ~1 billion iterations, reading ~12.8 GB past the chunk boundary, resulting in application crash or denial of service (GitHub Advisory).

Indicators of compromise

  • Process: Application using FluidSynth crashes or becomes unresponsive after loading a DLS file; abnormally high memory or CPU usage associated with the FluidSynth process during DLS file parsing.
  • File System: Presence of unexpected or externally sourced .dls files in directories accessible to the FluidSynth application.
  • Logs: Application crash logs or core dumps referencing FluidSynth's DLS parser; out-of-memory or segmentation fault errors in system logs coinciding with DLS file loading events.

Mitigation and workarounds

Upgrade FluidSynth to version 2.5.6, which contains the fix applied in commit 4d7084f. As a workaround for those unable to upgrade immediately, recompile FluidSynth with the CMake option enable-native-dls set to OFF, which disables the vulnerable native DLS parser entirely. Debian users should monitor their distribution's security tracker for an updated package, as no vendor-supplied patch was available at the time of initial disclosure (GitHub Advisory, OSV).

Community reactions

The vulnerability was credited to researcher TristanInSec (finder) and reported by FluidSynth maintainer derselbst, who published the advisory on July 4, 2026. Tenable released a Nessus detection plugin (ID 329570) shortly after disclosure. No broader media coverage or notable community commentary has been identified beyond the official advisory and automated vulnerability tracking (GitHub Advisory, Tenable).

Additional resources


SourceThis report was generated using AI

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