Security readout for executives and security teams
Plain-English summary
CVE-2021-29548 is a low-severity TensorFlow denial-of-service issue. A user able to trigger a specific raw TensorFlow operation can cause a division-by-zero runtime error, disrupting availability but not exposing or altering data.
Executive priority
Treat this as routine patching unless the organization offers shared or user-extensible TensorFlow execution. The main business risk is localized workload disruption, not data compromise or broad remote compromise based on available sources.
Technical view
The flaw is CWE-369 in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization. TensorFlow did not validate all constraints required by the operation contract, allowing a division by zero and process-level denial of service in affected supported releases.
Likely exposure
Exposure is most likely in ML services, notebooks, pipelines, or products running affected TensorFlow versions where low-privileged local users or submitted workloads can invoke the vulnerable raw op.
Exploitation context
The CVE is not listed as KEV, and the provided sources do not report active exploitation. CVSS indicates local access, high attack complexity, low privileges required, no user interaction, and only low availability impact.
Researcher notes
Focus analysis on version exposure and reachable use of QuantizedBatchNormWithGlobalNormalization. The advisory attributes impact to missing validation against the op contract; review the linked fix for exact validation changes when assessing forks or backports.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported backport release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where pinned to older branches.
- Check TensorFlow’s advisory for any branch-specific guidance before remediation.
- Restrict untrusted users from running arbitrary TensorFlow operations in shared environments.
Validation and detection
- Inventory deployed TensorFlow versions in applications, images, notebooks, and training workers.
- Flag versions below 2.1.4 and vulnerable 2.2.x, 2.3.x, and 2.4.x ranges.
- Identify services that expose TensorFlow execution to untrusted users or submitted workloads.
- Confirm upgraded environments use fixed TensorFlow packages before returning shared workloads to service.
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
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCVE-2021-29548 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
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-p45v-v4pw-77jrCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75bCVE 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.