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CVE-2019-3823 (GCVE-0-2019-3823)

Vulnerability from cvelistv5 – Published: 2019-02-06 20:00 – Updated: 2026-04-15 20:52
VLAI
Summary
libcurl versions from 7.34.0 to before 7.64.0 are vulnerable to a heap out-of-bounds read in the code handling the end-of-response for SMTP. If the buffer passed to `smtp_endofresp()` isn't NUL terminated and contains no character ending the parsed number, and `len` is set to 5, then the `strtol()` call reads beyond the allocated buffer. The read contents will not be returned to the caller.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-15 20:52 UTC
CWE
Impacted products
Vendor Product Version CPE status
The curl Project curl Affected: 7.64.0
guessed Create a notification for this product.
Date Public
2019-02-06 00:00
Show details on NVD website

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}



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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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Detection rules are retrieved from Rulezet.

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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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