Process crash via rank-zero DeserializeSparse input
Published Nov 5, 2021 · Updated Aug 4, 2024
Null pointer dereference in Google TensorFlow before 2.4.4, 2.5.0 through 2.5.1, and 2.6.0 allows local users to crash a process. DeserializeSparse shape inference assumes serialized_sparse has positive rank and reads its last dimension without first rejecting a scalar rank-zero tensor. Triggering the flaw requires the ability to supply a crafted TensorFlow graph for shape inference; the bounded impact is termination of the hosting process.
Summary
What happened
Null pointer dereference in Google TensorFlow before 2.4.4, 2.5.0 through 2.5.1, and 2.6.0 allows local users to crash a process. DeserializeSparse shape inference assumes serialized_sparse has positive rank and reads its last dimension without first rejecting a scalar rank-zero tensor. Triggering the flaw requires the ability to supply a crafted TensorFlow graph for shape inference; the bounded impact is termination of the hosting process.
The record
- CVE
- CVE-2021-41215
- Published
- Nov 5, 2021
- Updated
- Aug 4, 2024
- Vendor
- Product
- TensorFlow
- Classifications
- CWE-476, T1499
- Attack vector
- local
- Privileges
- authenticated
Timeline
How it unfolded
- Nov 5, 2021CVE publishedPublication date reported by the CVE source.
- Aug 4, 2024Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
Compare these published version ranges with your installed build and any vendor patches.
- Affected versionversion=< 2.4.4
- Affected versionversion=>= 2.5.0, < 2.5.2
- Affected versionversion=>= 2.6.0, < 2.6.1
What conditions does exploitation require?
What is affected?
Published CVSS scores
CVSS describes severity. EPSS estimates exploitation probability.
Attacks
What attackers are doing with it
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Public exploit references
- TensorFlow DeserializeSparse rank-zero reproducerproof of concept · demonstrated
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Technologies
Your stack
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Your stack
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