CVE-2019-17211 (GCVE-0-2019-17211)

Vulnerability from cvelistv5 – Published: 2019-11-05 15:06 – Updated: 2024-08-05 01:33
VLAI
Summary
An integer overflow was discovered in the CoAP library in Arm Mbed OS 5.14.0. The function sn_coap_builder_calc_needed_packet_data_size_2() is used to calculate the required memory for the CoAP message from the sn_coap_hdr_s data structure. Both returned_byte_count and src_coap_msg_ptr->payload_len are of type uint16_t. When added together, the result returned_byte_count can wrap around the maximum uint16_t value. As a result, insufficient buffer space is allocated for the corresponding CoAP message.
CWE
  • n/a
Assigner
mitre CNA under the mitre root
CNA scorecard E 29/100 over 2271 records in the last 180 days details
Date Public
2019-11-03 00:00
Show details on NVD website

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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.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
Events included in the timeline

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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.