CVE-2025-10164 (GCVE-0-2025-10164)
Vulnerability from cvelistv5 – Published: 2025-09-09 18:32 – Updated: 2025-09-10 19:43Title
lmsys sglang update_weights_from_tensor main deserialization
Summary
A security flaw has been discovered in lmsys sglang 0.4.6. Affected by this vulnerability is the function main of the file /update_weights_from_tensor. The manipulation of the argument serialized_named_tensors results in deserialization. The attack can be launched remotely. The exploit has been released to the public and may be exploited. The vendor was contacted early about this disclosure but did not respond in any way.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-09-10 19:43 UTC
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.323203 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.323203 | signaturepermissions-required |
| https://vuldb.com/?submit.635919 | third-party-advisory |
Shadowserver
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: a20dd26a-1170-4eab-b56f-a9176922aaf6
Exploited: Yes
Characteristics
Severity:
73.0
Timestamps
First Seen: 2026-08-22
Asserted: 2026-08-22
Last Seen: 2026-09-07
Scope
Asset Exposure: ['internet-facing']
Notes: Affected: lmsys / lmsys sglang | Class: ai-system | Severity: High (CVSS 7.3) | IoT: no | In CISA KEV: no | Honeypot connections on 2026-09-07: 15
Evidence
Type: Honeypot
Signal: In The Wild Attempts
Confidence: 70%
Source: shadowserver
Details
| 1D | 1 |
|---|---|
| Iot | no |
| Feed | Shadowserver Foundation honeypot/exploited-vulnerabilities |
| Type | http-scan |
| Class | ai-system |
| 7D Avg | 1 |
| Vendor | lmsys |
| 30D Avg | 0 |
| 90D Avg | 0 |
| Product | lmsys sglang |
| Cisa Kev | no |
| Connections | 15 |
| Observation Date | 2026-09-07 |
| Vulnerability Class | CVSS |
| Vulnerability Score | 7.3 |
| Vulnerability Severity | High |
References
Created: 2026-08-24 01:11 UTC
| Updated: 2026-09-09 01:01 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 5775be7d-1a29-41ee-bdff-b6026e93023f
Exploited: Yes
Timestamps
First Seen: 2026-08-22
Asserted: 2026-08-22
Scope
Notes: lmsys sglang update_weights_from_tensor main deserialization | Affected: lmsys / sglang | CVSS: 6.9 (MEDIUM) | EPSS: 0.00404 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: kevintel
Details
| Feed | KEVIntel (kevintel.com) |
|---|---|
| Title | lmsys sglang update_weights_from_tensor main deserialization |
| Vendor | lmsys |
| Product | sglang |
| Added Date | 2026-08-22T00:00:00.000Z |
| Cvss Score | 6.9 |
| Epss Score | 0.00404 |
| Cvss Severity | MEDIUM |
| Epss Percentile | 0.33952 |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-08-23 11:00 UTC
| Updated: 2026-08-23 11:00 UTC
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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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