TensorRT-LLM: Deserialization of Untrusted Data
CVE-2025-23254 is a high-severity deserialization of untrusted data vulnerability in TensorRT-LLM from NVIDIA, with a CVSS 3.1 base score of 8.8 out of 10. It can be exploited with local access to the system by an attacker with a low-privileged account, without any action from a user. Affected TensorRT-LLM versions: All versions prior to 0.18.2. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.28% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of TensorRT-LLM you run against the affected versions below, and install the vendor's latest release.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| NVIDIA | TensorRT-LLM | All versions prior to 0.18.2 | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- LocalThe attacker needs local access or must trick a user into running something.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- LowA basic user account is enough.
- User interaction
- NoneNo action by a victim is required.
- Scope
- ChangedThe attack can reach beyond the vulnerable component into other systems.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H. Scored by CNA (nvidia.com).
Official description
NVIDIA TensorRT-LLM for any platform contains a vulnerability in python executor where an attacker may cause a data validation issue by local access to the TRTLLM server. A successful exploit of this vulnerability may lead to code execution, information disclosure and data tampering.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
How developers prevent this class of flaw
- If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
- When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
- Explicitly define a final object() to prevent deserialization.
More Deserialization of Untrusted Data vulnerabilities (CWE-502)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: no, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Other references
Other vulnerabilities in TensorRT-LLM
- 7.1 High severity
- 8.4 High severity
- 7.1 High severity
- 7.4 High severity
- 7.8 High severity
- 7.5 High severity
Recent Deserialization of Untrusted Data vulnerabilities
- 7.8 High severity
- 9.8 Critical severity
- 8.8 High severity
- 7.8 High severity
- 8.1 High severity
- 7.2 High severity
Spotted an error? Tell us.