Security readout for executives and security teams
Plain-English summary
CVE-2021-29584 is a low-severity TensorFlow denial-of-service issue. A crafted tensor shape can trigger an integer overflow path that causes TensorFlow to abort with a CHECK failure. The known impact is availability disruption, not data theft or code execution.
Executive priority
Treat as routine patching unless vulnerable TensorFlow workloads are shared, multi-user, or accept untrusted inputs. It can interrupt ML services, but available evidence does not indicate data exposure, privilege escalation, or active exploitation.
Technical view
TensorFlow SparseSplit builds a dense shape without validating overflow. The TensorShape constructor can CHECK-fail when InitDims returns an error, terminating the process. Sources name CWE-190 and affected TensorFlow ranges before patched releases 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0.
Likely exposure
Exposure is limited to systems running affected TensorFlow versions where an attacker can influence tensor shapes reaching the vulnerable SparseSplit path. CVSS marks exploitation as local, high complexity, requiring low privileges, with low availability impact only.
Exploitation context
The source bundle does not show active exploitation, and KEV status is false. The issue is a crash condition from malformed or oversized shape handling, useful mainly for disrupting vulnerable ML workloads rather than compromising confidentiality or integrity.
Researcher notes
Focus analysis on SparseSplit shape construction and TensorShape overflow handling. Avoid assuming remote exposure without application-specific input paths. The advisory states legacy constructors should use status-returning shape builders to prevent CHECK-triggered aborts.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or the patched supported branch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
- Inventory containers, notebooks, services, and training jobs for affected TensorFlow versions.
- Check TensorFlow vendor guidance before using older unsupported releases.
- Reduce exposure to untrusted tensor inputs until patched.
Validation and detection
- Confirm deployed TensorFlow versions are not in the listed affected ranges.
- Review dependency lockfiles and container images for transitive TensorFlow installations.
- Check crash logs for TensorShape, SparseSplit, InitDims, or CHECK-failure patterns.
- Verify patched builds include the referenced TensorFlow fix commit.
- Document remaining affected workloads and their input trust boundaries.
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-190: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29584 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-xvjm-fvxx-q3hvCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60CVE 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.
Integer Overflow or Wraparound
Integer Overflow or Wraparound represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.