Security readout for executives and security teams
Plain-English summary
CVE-2021-29527 is a low-severity TensorFlow flaw where a caller can trigger a divide-by-zero condition in QuantizedConv2D. The documented impact is limited availability loss, such as a crash or failed computation, not data theft or integrity compromise.
Executive priority
Treat as routine patch management unless affected TensorFlow workloads process untrusted local inputs or run in shared compute environments. Prioritize shared ML infrastructure where a crash could disrupt production or tenant workloads.
Technical view
TensorFlow tf.raw_ops.QuantizedConv2D divided by a caller-controlled quantity in quantized_conv_ops.cc, creating CWE-369 divide-by-zero exposure. The CVSS 3.1 score is 2.5 with local access, high attack complexity, low privileges, no user interaction, and low availability impact only.
Likely exposure
Exposure is most likely in environments running affected TensorFlow versions and allowing low-privileged local users or controlled callers to invoke QuantizedConv2D with crafted parameters. Internet-facing exposure is not established by the sources.
Exploitation context
The provided sources do not identify active exploitation, and this CVE is not marked KEV. Exploitation requires local access, low privileges, and high complexity according to the supplied CVSS vector.
Researcher notes
The issue is narrowly scoped to a divide-by-zero in QuantizedConv2D from a caller-controlled divisor. The sources name the fix commit and fixed release lines, but do not provide evidence of remote exploitation or confidentiality impact.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or a fixed supported patch release.
- For 2.4.x, upgrade to at least TensorFlow 2.4.2.
- For 2.3.x, upgrade to at least TensorFlow 2.3.3.
- For 2.2.x, upgrade to at least TensorFlow 2.2.3.
- For 2.1.x or older supported deployments, check vendor guidance for 2.1.4.
Validation and detection
- Inventory TensorFlow versions across notebooks, services, batch jobs, and ML build images.
- Flag versions below 2.1.4 and affected 2.2.x, 2.3.x, and 2.4.x ranges.
- Confirm whether QuantizedConv2D or tf.raw_ops.QuantizedConv2D is reachable in local workloads.
- Verify upgraded environments report fixed TensorFlow versions before redeployment.
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-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29527 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-x4g7-fvjj-prg8CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036bCVE 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.
Divide By Zero
Divide By Zero represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.