TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.io.decode_raw produces incorrect results and crashes the Python interpreter when combining fixed_length and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the fixed_length value to the size of the type argument. The fixed_length argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the out_data pointer by fixed_length * sizeof(T) bytes whereas it only copied at most fixed_length bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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.
CVE-2021-29614
This high-severity CVE scores 7.1 under NVD CVSS v3. EPSS exploit probability: 0.2%, top 87% of all CVEs by exploit prediction. GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).
- High severity, but no confirmed exploitation yet
No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.
- CVSS v3
- 7.1
- EG Score
- 7.1(medium)
- EG Risk
- 37(Track)EG Risk 37/100SSVC: Track
EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).
How it’s computedSeverity71% × 45%Exploitation0% × 40%Automatability30% × 15%Action: Routine — remediate on your standard cadence. - EPSS
- 12.8%
- KEV
- Not listed
Published
May 14, 2021
Last Modified
November 21, 2024
References (4)
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h
Affected Packages
(3 across 1 ecosystem)
PyPI(3)
| Package | Vulnerable range | Fixed in | Dependents |
|---|---|---|---|
| tensorflow | 0.12.0 ... 2.4.1 (98 versions) | 2.4.2 | — |
| tensorflow-cpu | 1.15.0 ... 2.4.1 (13 versions) | 2.4.2 | — |
| tensorflow-gpu | 0.12.0 ... 2.4.1 (48 versions) | 2.4.2 | — |
Weakness Classification(2)
MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.
Data Freshness Timeline
(refreshed 14× in last 7d / 51× in last 30d)
Each row is a source pipeline that fetched or updated this CVE on that date, with what changed. For example, "NVD update" means NVD published or revised its analysis for this CVE; "MITRE cvelistV5" means we ingested or refreshed it from the CNA feed. Most recent first.
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Related CVEs(same CWE)
Same CWE
10 shownCWE-787
- CVE-2007-0158EG 9.8CRITICAL
- CVE-2007-0899EG 9.8CRITICAL
- CVE-2011-2462EG 9.8 KEVEPSS 100%CRITICAL
- CVE-2010-20115EG 9.3CRITICAL
- CVE-2010-4398NVD 7.8EG 9.0 KEVEPSS 95%HIGH
- CVE-2010-3333NVD 7.8EG 9.0 KEVEPSS 100%HIGH
- CVE-2010-2883NVD 7.3EG 9.0 KEVEPSS 100%HIGH
- CVE-2010-1297NVD 7.8EG 9.0 KEVEPSS 100%HIGH
- CVE-2009-3953NVD 8.8EG 9.0 KEVEPSS 100%HIGH
- CVE-2009-3129NVD 7.8EG 9.0 KEVEPSS 100%HIGH
Frequently asked(5)
What is CVE-2021-29614?
When was CVE-2021-29614 disclosed?
Is CVE-2021-29614 actively exploited?
What is the CVSS score of CVE-2021-29614?
How do I remediate CVE-2021-29614?
Dependency Blast Radius
See which npm, PyPI, Go, and Maven packages are affected by CVE-2021-29614
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