CVE-2024-3119 (GCVE-0-2024-3119)

Vulnerability from cvelistv5 – Published: 2024-04-09 23:55 – Updated: 2024-08-08 21:20
VLAI
Title
Stack-Buffer Overflow in 'Call-ID' and 'X-Call-ID' SIP Header Processing in sngrep
Summary
A buffer overflow vulnerability exists in all versions of sngrep since v0.4.2, due to improper handling of 'Call-ID' and 'X-Call-ID' SIP headers. The functions sip_get_callid and sip_get_xcallid in sip.c use the strncpy function to copy header contents into fixed-size buffers without checking the data length. This flaw allows remote attackers to execute arbitrary code or cause a denial of service (DoS) through specially crafted SIP messages.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-04-10 19:23 UTC
CWE
  • CWE-120 - Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')
Impacted products
Vendor Product Version CPE status
irontec sngrep Affected: 0.4.2 , ≤ 1.8.0 (semver)
guessed Create a notification for this product.
irontec sngrep Affected: 0.4.2 , ≤ 1.8.0 (custom)
    cpe:2.3:a:irontec:sngrep:0.4.2:*:*:*:*:*:*:*
Create a notification for this product.
Date Public
2024-04-09 23:10
Show details on NVD website

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  • 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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