CVE-2016-9158 (GCVE-0-2016-9158)
Vulnerability from cvelistv5 – Published: 2016-12-17 03:34 – Updated: 2026-06-02 18:33
VLAI
EPSS
VEX
Summary
A vulnerability has been identified in SIMATIC S7-300 CPU family (All versions), SIMATIC S7-300 CPU family (incl. related ET200 CPUs and SIPLUS variants) (All versions), SIMATIC S7-400 PN/DP V6 and below CPU family (incl. SIPLUS variants) (All versions), SIMATIC S7-400 PN/DP V7 CPU family (incl. SIPLUS variants) (All versions), SIMATIC S7-400 V6 and earlier CPU family (All versions), SIMATIC S7-400 V7 CPU family (All versions). Specially crafted packets sent to port 80/tcp could cause the affected devices to go into defect mode. A cold restart is required to recover the system.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-02 18:32 UTC
CWE
- CWE-20 - Improper Input Validation
Assigner
References
5 references
| URL | Tags |
|---|---|
| http://www.securityfocus.com/bid/94820 | vdb-entryx_refsource_BID |
| http://www.securitytracker.com/id/1037434 | vdb-entryx_refsource_SECTRACK |
| https://ics-cert.us-cert.gov/advisories/ICSA-16-348-05 | x_refsource_MISC |
| https://www.siemens.com/cert/pool/cert/siemens_se… | x_refsource_CONFIRM |
| https://cert-portal.siemens.com/productcert/pdf/s… | x_refsource_MISC |
Impacted products
6 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Siemens AG | SIMATIC S7-300 CPU family |
Affected:
All versions
|
guessed | |
| Siemens AG | SIMATIC S7-300 CPU family (incl. related ET200 CPUs and SIPLUS variants) |
Affected:
All versions
|
guessed | |
| Siemens AG | SIMATIC S7-400 PN/DP V6 and below CPU family (incl. SIPLUS variants) |
Affected:
All versions
|
guessed | |
| Siemens AG | SIMATIC S7-400 PN/DP V7 CPU family (incl. SIPLUS variants) |
Affected:
All versions
|
guessed | |
| Siemens AG | SIMATIC S7-400 V6 and earlier CPU family |
Affected:
All versions
|
guessed | |
| Siemens AG | SIMATIC S7-400 V7 CPU family |
Affected:
All versions
|
guessed |
Date Public
2016-12-16 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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