Security readout for executives and security teams
Plain-English summary
CVE-2021-29540 is a low-severity TensorFlow flaw in a convolution backpropagation operation. A local, low-privileged attacker who can run crafted TensorFlow workloads may trigger a heap buffer overflow and cause limited availability impact. The sources do not indicate data theft, integrity compromise, remote exploitation, or known active exploitation.
Executive priority
Treat this as routine patch management unless affected TensorFlow runs in shared or user-submitted workload environments. Business urgency is lower than internet-facing or data-exposure vulnerabilities, but outdated ML stacks should be upgraded during the next maintenance window.
Technical view
Conv2DBackpropFilter computes filter tensor size without validating it against the element count in filter_sizes. Later reads or writes use the computed size, creating a heap buffer overflow condition. Affected TensorFlow ranges include 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.
Likely exposure
Exposure is most likely in ML services, notebooks, CI jobs, or research systems running affected TensorFlow versions where local users or submitted workloads can execute TensorFlow operations. Purely static deployments without attacker-controlled model execution are less likely to be exposed.
Exploitation context
The CVSS vector requires local access, low privileges, no user interaction, high attack complexity, and indicates availability impact only. The provided sources mark KEV as false and do not report active exploitation or public weaponization.
Researcher notes
The key weakness is a size-validation mismatch in Conv2DBackpropFilter leading to heap buffer overflow. Evidence supports local, high-complexity availability risk only. The bundle does not provide exploit status beyond KEV false, and no additional affected products should be inferred.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
- Inventory ML containers, notebooks, training workers, and CI images for TensorFlow versions.
- Restrict untrusted users from running arbitrary TensorFlow workloads on shared systems.
- Monitor the TensorFlow advisory for any later guidance.
Validation and detection
- Confirm the TensorFlow version used by each application and runtime image.
- Compare discovered versions with the affected ranges listed in the advisory.
- Check dependency lockfiles, SBOMs, notebooks, and container build manifests.
- Verify production and research environments are running patched versions.
- Document any legacy unsupported TensorFlow usage for risk acceptance or upgrade planning.
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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Open ATT&CK lookupCVE-2021-29540 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-xgc3-m89p-vr3xCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96CVE 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.
Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')
Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.