CVE-2026-11826 (GCVE-0-2026-11826)
Vulnerability from cvelistv5 – Published: 2026-07-18 13:35 – Updated: 2026-07-28 01:48
VLAI
EPSS
VEX
Title
OpenPLC_v3 Heap-Based Buffer Overflow in Modbus Master getData()
Summary
OpenPLC_v3 contains a heap-based buffer overflow in the getData() function in webserver/core/modbus_master.cpp. getData() reads characters between two delimiters into a caller-supplied buffer with no size parameter and no bounds check. In parseConfig() the function is invoked with the 100-byte heap-allocated MB_device.dev_name field. An authenticated attacker with access to the OpenPLC web interface can send a crafted HTTP POST to the /modbus endpoint with an oversized device_name value; the value is persisted to mbconfig.cfg and parsed on load, overflowing dev_name and overwriting adjacent struct fields (protocol at offset 108, dev_address at offset 109, ip_port at offset 210). A 200-byte payload writes 100 bytes past the allocation. The result is heap corruption leading to runtime crash and denial of service of the PLC process control loop, with attacker-controlled overwrite of adjacent configuration fields. The upstream repository was archived on 2026-04-04 and no fix is expected; the vendor has confirmed the issue does not affect OpenPLC Runtime v4.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-20 17:02 UTC
CWE
- CWE-122 - Heap-based Buffer Overflow
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://gist.github.com/Shukhrat-03/5cf1825b72e48… | technical-description |
| https://github.com/thiagoralves/OpenPLC_v3/commit… | patch |
| https://github.com/thiagoralves/OpenPLC_v3 | product |
| https://www.vulncheck.com/advisories/openplc-v3-h… | third-party-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| openplcproject | OpenPLC_v3 |
Affected:
0 , ≤ b470206
(git)
cpe:2.3:h:openplcproject:openplc_v3:*:*:*:*:*:*:*:* |
Date Public
2026-07-17 00:00
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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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