tensorflow: Heap-based Buffer Overflow
CVE-2023-25668 is a critical-severity heap-based buffer overflow vulnerability in tensorflow, with a CVSS 3.1 base score of 9.8 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected tensorflow versions: < 2.11.1. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.84% chance of exploitation within 30 days, higher than 58% of scored CVEs.
Critical severity: a successful attack would have severe impact.
What to do
- Check which version of tensorflow you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on github.com for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| TensorFlow | tensorflow | < 2.11.1 | 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
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- 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:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 9.8 | Critical | CNA (github.com) |
| CVSS 3.1 | 9.8 | Critical | NVD |
Official description
TensorFlow is an open source platform for machine learning. Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution. The fix will be included in TensorFlow version 2.12.0 and will also cherrypick this commit on TensorFlow version 2.11.1.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Heap-based Buffer Overflow
The product writes past the end of a buffer that was allocated on the heap.
How developers prevent this class of flaw
- Pre-design: Use a language or compiler that performs automatic bounds checking.
- Use an abstraction library to abstract away risky APIs. Not a complete solution.
- Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking. D3-SFCV (Stack Frame Canary Validation) from D3FEND discusses canary-based detection in detail.
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: yes, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Patches and release notes
Other vulnerabilities in tensorflow
- 7.8 High severity
- 7.5 High severity
- 9.8 Critical severity
- 7.5 High severity
- 7.8 High severity
- 7.5 High severity
Recent Heap-based Buffer Overflow vulnerabilities
- 7.8 High severity
- 7.8 High severity
- 7.8 High severity
- 7.8 High severity
- 7.8 High severity
- 7.8 High severity
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