ModelTC LightLLM up to 1.2.0 NCCL control channel exposed_set_value memory allocation
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.3 | $0-$5k | 0.83- |
Summary
A vulnerability identified as problematic has been detected in ModelTC LightLLM up to 1.2.0. Affected by this issue is the function exposed_set_value of the component NCCL control channel. Performing a manipulation results in memory allocation.
This vulnerability is cataloged as CVE-2026-103042. It is possible to initiate the attack remotely. There is no exploit available.
Details
A vulnerability was found in ModelTC LightLLM up to 1.2.0. It has been classified as problematic. This affects the function exposed_set_value of the component NCCL control channel. The manipulation with an unknown input leads to a memory allocation vulnerability. CWE is classifying the issue as CWE-789. The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated. This is going to have an impact on availability. The summary by CVE is:
LightLLM through 1.2.0 contains a memory exhaustion vulnerability in the NCCL control channel when started with --pd_trans_mode nccl, allowing unauthenticated attackers to exhaust KV-transfer worker memory. Attackers can call the exposed_set_value method to store unbounded key-value pairs without size limits, causing the worker process to crash and triggering node failure.
It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2026-103042 since 09/30/2026. The exploitability is told to be easy. It is possible to initiate the attack remotely. No form of authentication is needed for exploitation. Technical details of the vulnerability are known, but there is no available exploit.
There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Vendor
Name
Version
Website
- Product: https://github.com/ModelTC/LightLLM/
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒
CVSSv3
VulDB Meta Base Score: 6.4VulDB Meta Temp Score: 6.3
VulDB Base Score: 5.3
VulDB Temp Score: 5.1
VulDB Vector: 🔒
VulDB Reliability: 🔍
CNA Base Score: 7.5
CNA Vector (VulnCheck): 🔒
CVSSv2
| AV | AC | Au | C | I | A |
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Memory allocationCWE: CWE-789 / CWE-400 / CWE-404
CAPEC: 🔒
ATT&CK: 🔒
Physical: No
Local: No
Remote: Yes
Availability: 🔒
Status: Not defined
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: no mitigation knownStatus: 🔍
0-Day Time: 🔒
Timeline
09/29/2026 VulDB entry created09/30/2026 Advisory disclosed
09/30/2026 CVE reserved
09/30/2026 VulDB entry last update
Sources
Product: github.comAdvisory: github.com
Status: Not defined
CVE: CVE-2026-103042 (🔒)
GCVE (CVE): GCVE-0-2026-103042
GCVE (VulDB): GCVE-100-411819
Entry
Created: 09/30/2026 01:13Changes: 09/30/2026 01:13 (75)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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