CVE-2026-77014 (GCVE-0-2026-77014)

Vulnerability from cvelistv5 – Published: 2026-08-20 08:21 – Updated: 2026-08-25 14:23
VLAI
Title
Libsoup: libsoup: integer truncation in sort_ranges() comparator causes silent omission of http range responses
Summary
A flaw was found in libsoup's SoupServer HTTP Range header processing. The sort_ranges() comparator in soup-message-headers.c truncates a 64-bit subtraction result to 32-bit int, flipping the sign for range offsets differing by more than INT_MAX. This causes silent omission of requested byte ranges from HTTP 206 Partial Content responses on resources larger than approximately 2 GB.
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-25 14:23 UTC
CWE
  • CWE-197 - Numeric Truncation Error
Impacted products
Date Public
2026-07-13 00:00
Show details on NVD website

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Tags
Taxonomy of the tags.


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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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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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