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CVE Record

CVE-2021-29531: CHECK-fail in tf.raw_ops.EncodePng

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

This TensorFlow flaw can crash a process when PNG encoding receives an empty input tensor. The impact is denial of service only: no data theft or code execution is described. Business urgency is low unless TensorFlow image encoding is exposed to untrusted users in production workflows.

Executive priority

Treat this as a low-priority availability risk unless affected TensorFlow image encoding is reachable from tenant, customer, or batch-ingestion inputs. Patch during the next normal dependency maintenance window, faster for exposed ML services.

Technical view

tf.raw_ops.EncodePng accepted an empty pixel tensor, producing a null data buffer passed into png::WriteImageToBuffer. A CHECK_NOTNULL failure then aborts the process and prints a stack trace. The issue is CWE-754 and affects specific TensorFlow branches before patched releases.

Likely exposure

Exposure is most likely in applications using affected TensorFlow versions and allowing untrusted or semi-trusted inputs to reach PNG encoding. The CVSS vector indicates local access, high complexity, low privileges, no user interaction, and low availability impact.

Exploitation context

The source bundle does not show active exploitation, and KEV is false. The described outcome is a controlled process abort from an empty tensor, not code execution. Practical risk depends on whether attackers can influence tensors passed into TensorFlow PNG encoding.

Researcher notes

Focus validation on version ranges and reachable EncodePng call sites. The advisory identifies incomplete exceptional-condition handling: overflow checks existed, but empty tensors were not rejected before dereferencing the buffer in PNG writing.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or patched releases 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Add validation rejecting empty tensors before PNG encoding.
  • Restrict untrusted callers from image encoding paths until patched.
  • Check TensorFlow vendor guidance for branch-specific remediation details.

Validation and detection

  • Inventory deployed TensorFlow versions and compare them with affected ranges.
  • Review application paths using tf.raw_ops.EncodePng or PNG encoding wrappers.
  • Confirm user-supplied image tensors cannot be empty before encoding.
  • Run regression tests for empty-tensor handling after upgrade.
Prepared
Confidence
high
Sources
4

Public sources used

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

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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cwe · low confidence lookup

CWE-754: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29531 mapping review

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Vulnerability profileCVE Program record
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

Official CVE source material

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.

1CVSS vectors
0Timeline events
0ADP providers
3Source links

CVSS vector scores

1 official score

We 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.

ScoreVersionSeverityVectorExploitImpactSource
2.5CVSS 3.1LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

2.5Low
CVSS 3.1 vector shape for CVE-2021-29531Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
tensorflowtensorflow< 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-754

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-754 · source CWE mapping

Improper Check for Unusual or Exceptional Conditions

Improper Check for Unusual or Exceptional Conditions represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

Open CWE intelligenceSame-CWE CVEs