CVE-2026-10773 (GCVE-0-2026-10773)
Vulnerability from cvelistv5 – Published: 2026-08-01 12:21 – Updated: 2026-08-31 23:13
VLAI
EPSS
VEX
Title
Out-of-bounds read in DHCPv4 client message-type name lookup (net_dhcpv4_msg_type_name)
Summary
The DHCPv4 client helper net_dhcpv4_msg_type_name() in subsys/net/lib/dhcpv4/dhcpv4.c indexes a static 8-element const char * name table after a faulty bounds check. The guard used msg_type <= sizeof(name) instead of msg_type <= ARRAY_SIZE(name); sizeof returns the byte size of the pointer array (32 on 32-bit, 64 on 64-bit targets) rather than the element count of 8, so message-type values from 9 up to that byte size pass the check and cause name[msg_type - 1] to read past the end of the array.
The msg_type value originates from the DHCP MESSAGE TYPE option, which is read as an unchecked raw byte from a received packet (net_pkt_read_u8) and passed unmodified into the lookup. A DHCP server, or any host able to inject a spoofed DHCP reply onto the client's link, can therefore drive the index out of bounds. The out-of-range slot yields a garbage const char * that is then dereferenced by a %s log conversion.
The lookup is reached only from a debug log statement (NET_DBG / LOG_DBG), so the out-of-bounds read is triggerable only when the DHCPv4 log module is built at DEBUG level (CONFIG_NET_DHCPV4_LOG_LEVEL_DBG), which is not the default configuration. When that condition holds, the result is an out-of-bounds read and a wild-pointer dereference: most likely a crash of the DHCP client (denial of service) and potentially disclosure of an adjacent pointer's contents through the log output. The fix replaces sizeof with ARRAY_SIZE, restoring the correct 1..8 acceptance window.
Severity
5.4 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-03 17:50 UTC
Assigner
References
2 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| zephyrproject | zephyr |
Affected:
1.7.0 , < 4.4.2
(semver)
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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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