CVE-2026-33416 (GCVE-0-2026-33416)
Vulnerability from cvelistv5 – Published: 2026-03-26 16:48 – Updated: 2026-04-01 03:55
VLAI
EPSS
VEX
Title
LIBPNG has use-after-free via pointer aliasing in `png_set_tRNS` and `png_set_PLTE`
Summary
LIBPNG is a reference library for use in applications that read, create, and manipulate PNG (Portable Network Graphics) raster image files. In versions 1.2.1 through 1.6.55, `png_set_tRNS` and `png_set_PLTE` each alias a heap-allocated buffer between `png_struct` and `png_info`, sharing a single allocation across two structs with independent lifetimes. The `trans_alpha` aliasing has been present since at least libpng 1.0, and the `palette` aliasing since at least 1.2.1. Both affect all prior release lines `png_set_tRNS` sets `png_ptr->trans_alpha = info_ptr->trans_alpha` (256-byte buffer) and `png_set_PLTE` sets `info_ptr->palette = png_ptr->palette` (768-byte buffer). In both cases, calling `png_free_data` (with `PNG_FREE_TRNS` or `PNG_FREE_PLTE`) frees the buffer through `info_ptr` while the corresponding `png_ptr` pointer remains dangling. Subsequent row-transform functions dereference and, in some code paths, write to the freed memory. A second call to `png_set_tRNS` or `png_set_PLTE` has the same effect, because both functions call `png_free_data` internally before reallocating the `info_ptr` buffer. Version 1.6.56 fixes the issue.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-31 00:00 UTC
CWE
- CWE-416 - Use After Free
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/pnggroup/libpng/security/advis… | x_refsource_CONFIRM |
| https://github.com/pnggroup/libpng/pull/824 | x_refsource_MISC |
| https://github.com/pnggroup/libpng/commit/2301926… | x_refsource_MISC |
| https://github.com/pnggroup/libpng/commit/7ea9eea… | x_refsource_MISC |
| https://github.com/pnggroup/libpng/commit/a3a2144… | x_refsource_MISC |
| https://github.com/pnggroup/libpng/commit/c1b0318… | x_refsource_MISC |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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