Security readout for executives and security teams
Plain-English summary
CVE-2021-29582 is a low-severity TensorFlow memory safety flaw. Malformed inputs to tf.raw_ops.Dequantize can make TensorFlow read beyond heap-allocated data. The published impact is availability only, not data theft or code execution. It still matters where TensorFlow runs untrusted workloads or model-processing jobs.
Executive priority
Treat as routine patching unless TensorFlow is exposed to untrusted ML jobs or shared compute users. The issue has low severity and no cited active exploitation, but vulnerable ML platforms should be updated during the next controlled maintenance window.
Technical view
The Dequantize kernel accessed min_range and max_range tensors in parallel without validating matching shapes, causing a heap out-of-bounds read. The issue is classified as CWE-125 with CVSS 3.1 score 2.5, requiring local access, low privileges, high complexity, and affecting availability only.
Likely exposure
Exposure is limited to TensorFlow deployments on affected versions: before 2.1.4, 2.2.x before 2.2.3, 2.3.x before 2.3.3, and 2.4.x before 2.4.2. Risk is higher for systems processing untrusted TensorFlow operations, models, tensors, or user-controlled ML inputs.
Exploitation context
The source bundle does not show CISA KEV listing or evidence of active exploitation. The CVSS vector indicates exploitation is local, high complexity, requires low privileges, and does not require user interaction. Sources describe an out-of-bounds read trigger, but not a public weaponized exploit.
Researcher notes
The key condition is shape mismatch between min_range and max_range tensors in tf.raw_ops.Dequantize. The provided commit is the authoritative fix reference. Evidence supports availability impact only; do not expand scope to confidentiality, integrity, remote exploitation, or code execution without additional sources.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported patch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches are required.
- Restrict untrusted users from submitting arbitrary TensorFlow operations or model artifacts.
- Check TensorFlow security advisory guidance for environment-specific recommendations.
Validation and detection
- Inventory TensorFlow versions across applications, notebooks, containers, and training pipelines.
- Flag affected version ranges listed in the advisory and CVE record.
- Review whether untrusted inputs can reach tf.raw_ops.Dequantize.
- Confirm patched versions are deployed in runtime and build environments.
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
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-125: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29582 mapping review
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Open ATT&CK lookup- Severity
- Low
- CVSS
- 2.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
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 vector scores
1 official scoreWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
2.5LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c45w-2wxr-pp53CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1bCVE reference · x_refsource_MISC
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.
Out-of-bounds Read
Out-of-bounds Read represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.