CVE-2020-37239
Description
libbabl 0.1.62 contains a broken double free detection vulnerability that allows attackers to bypass memory safety checks by exploiting signature overwriting in freed chunks. Attackers can call babl_free() twice on the same pointer without triggering detection, as libc's malloc metadata overwrites babl's signature field upon freeing, enabling potential memory corruption and code execution.
AI Insight
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libbabl 0.1.62 has a broken double free detection that allows attackers to bypass memory safety checks, leading to potential memory corruption and code execution.
## Vulnerability libbabl 0.1.62 and newer [2] implements a custom memory allocator that uses a signature field in BablAllocInfo to detect double frees. When babl_free() is called, the signature is set to a freed string; however, libc's free() overwrites this signature with its own metadata, rendering the detection ineffective [1][2][4]. This allows an attacker to call babl_free() twice on the same pointer without triggering the double free warning.
Exploitation
An attacker can exploit this by causing a double free condition. The proof-of-concept [2] demonstrates a simple program that allocates memory with babl_malloc(), frees it once, then frees it again. The second free goes undetected because the signature has been overwritten. No special privileges or user interaction are required beyond triggering the double free in an application using libbabl.
Impact
A successful double free can corrupt heap metadata, leading to memory corruption and potentially arbitrary code execution [4]. Since libbabl is used by GEGL and GIMP [3], an attacker could compromise these applications, potentially gaining the same privileges as the user running them. In network-facing scenarios, this could lead to remote code execution.
Mitigation
No official fix has been released as of the publication date. Users should monitor the babl project [1] for a patched version. Workarounds are not available; the vulnerability is inherent to the design of the double free detection mechanism.
AI Insight generated on May 21, 2026. Synthesized from this CVE's description and the cited reference URLs; citations are validated against the source bundle.
Affected products
1Patches
0No patches discovered yet.
Vulnerability mechanics
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References
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