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CVE-2026-115924EUVD-2026-110073GHSA-QR6X-7V33-6QMG

Rembg: Unauthenticated SAM model injection permits local-file disclosure through malicious ONNX graphs

Published
Last modified
published
Not listed in CISA KEV
Fix
Fixed in 2.0.82
None on Exploit-DB, Metasploit or GitHub

Summary

Rembg, an open-source tool for removing image backgrounds, is affected, specifically its public API endpoint used for SAM model handling. An unauthenticated attacker can exploit this to read arbitrary files accessible to the Rembg process and run arbitrary computations through malicious model files.

Summarized by anthropic/claude-haiku-5.5. Verify details against the original sources.

Read the original description

Rembg is a tool to remove images background. Prior to 2.0.82, the public /api/remove endpoint passes attacker-controlled extras.sam_model through rembg/commands/s_command.py to rembg/sessions/sam.py, where it is interpolated into SAM filenames and GitHub download URLs passed to pooch.retrieve() without a pinned hash. An unauthenticated attacker can supply a sam_model value combining path traversal with URL fragment syntax to exploit the HTTP request discarding the fragment while the local POSIX path retains it. The first request primes the attacker-selected path tree, and an identical repeated request downloads malicious ONNX encoder and decoder files beside a readable target file. The graphs can load the target through ONNX external data and encode its bytes into the returned PNG mask, disclosing complete files accessible to the rembg process and allowing arbitrary ONNX computation. This issue is fixed in version 2.0.82.

Timeline

  1. CVE published
  2. First seen by cve.now

Affected versions

  • danielgatis rembg

    fixed in 2.0.82

  • rembgPyPI

    fixed in 2.0.82

References 3

GitHub · danielgatis/rembg