Security readout for executives and security teams
Plain-English summary
This vulnerability affects older TensorFlow versions before 1.7.1. A crafted configuration file could make the XLA compiler crash or read memory from elsewhere in the same process. The main business concern is legacy machine-learning infrastructure that processes untrusted model or compiler configuration inputs.
Executive priority
Prioritize this for legacy ML platforms, shared research environments, and pipelines ingesting outside model artifacts. It is not KEV-listed and lacks severity scoring, but memory read and crash potential justify cleanup where old TensorFlow remains in use.
Technical view
CVE-2018-10055 is an invalid memory access and/or heap buffer overflow in TensorFlow's XLA compiler before version 1.7.1. The documented impact is process crash or unintended reads from process memory through a crafted configuration file. The source bundle does not provide CVSS, CWE, or detailed affected package metadata.
Likely exposure
Exposure is most likely in legacy TensorFlow deployments before 1.7.1, especially systems using XLA with configuration files from untrusted or weakly controlled sources. Modern supported TensorFlow deployments are less likely exposed if they are beyond the fixed version.
Exploitation context
The provided sources do not show active exploitation, and the CVE is not listed as KEV. The described attack requires a crafted configuration file reaching TensorFlow's XLA compiler. No public exploit status, privilege requirements, or network exposure details are provided in the bundle.
Researcher notes
Evidence is limited to the CVE description and TensorFlow advisory reference. The bundle names TensorFlow before 1.7.1 and XLA, but does not provide CVSS, CWE classification, detailed affected builds, or exploit maturity. Avoid broader product claims without additional vendor evidence.
Mitigation direction
- Upgrade TensorFlow deployments before 1.7.1 to 1.7.1 or later.
- Check TensorFlow security advisory TFSA-2018-006 for vendor guidance.
- Restrict untrusted configuration files from reaching XLA compilation paths.
- Retire unsupported legacy TensorFlow environments where upgrade is impractical.
Validation and detection
- Inventory TensorFlow versions across production, research, and CI environments.
- Identify workloads using TensorFlow XLA compiler functionality.
- Review whether model or compiler configuration files come from untrusted sources.
- Confirm vulnerable deployments are upgraded beyond TensorFlow 1.7.1.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
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CVE-2018-10055 mapping review
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Open ATT&CK lookup- Severity
- Unknown
- CVSS
- Not scored
- Known Exploited
- No
- Published
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
CVSS and timeline data
No CVSS vectors or timeline events were available in the normalized CVE source material.
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-006.mdCVE reference · x_refsource_CONFIRM
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.