CVE-2019-1010299 (GCVE-0-2019-1010299)

Vulnerability from cvelistv5 – Published: 2019-07-15 17:18 – Updated: 2024-08-05 03:07
VLAI
Summary
The Rust Programming Language Standard Library 1.18.0 and later is affected by: CWE-200: Information Exposure. The impact is: Contents of uninitialized memory could be printed to string or to log file. The component is: Debug trait implementation for std::collections::vec_deque::Iter. The attack vector is: The program needs to invoke debug printing for iterator over an empty VecDeque. The fixed version is: 1.30.0, nightly versions after commit b85e4cc8fadaabd41da5b9645c08c68b8f89908d.
Severity
5.3 (Medium)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
CWE
Assigner
References
Impacted products
Vendor Product Version
The Rust Programming Language Standard Library Affected: 1.18.0 and later [fixed: 1.30.0, nightly versions after commit b85e4cc8fadaabd41da5b9645c08c68b8f89908d]
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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.



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.