CVE-2019-1010022 (GCVE-0-2019-1010022)

Vulnerability from cvelistv5 – Published: 2019-07-15 03:00 – Updated: 2024-11-15 15:04 Disputed
VLAI
Summary
GNU Libc current is affected by: Mitigation bypass. The impact is: Attacker may bypass stack guard protection. The component is: nptl. The attack vector is: Exploit stack buffer overflow vulnerability and use this bypass vulnerability to bypass stack guard. NOTE: Upstream comments indicate "this is being treated as a non-security bug and no real threat.
Severity
No CVSS data available.
SSVC
Exploitation: none Automatable: yes Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-04-24 16:01 UTC
CWE
  • Mitigation bypass
Assigner
dwf
References
Impacted products
Vendor Product Version CPE status
GNU C Library glibc Affected: current (At least as of 2018-02-16)
guessed Create a notification for this product.
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Taxonomy of the tags.





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.