Segmentation faultin TensorFlow when converting a Python string to tf.float16
In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.
Noroxi analysis
No Noroxi analysis for this record yet
We don't hand-write analysis for the hundreds of thousands of vulnerabilities in the database; that wouldn't be honest. For notable, high-impact vulnerabilities our team writes the mechanism, detection and remediation steps.
We use this product, ask for help- Published
- Jan 28, 2020
- Updated
- Jun 16, 2026
- EPSS
- 0.6% · 46th percentile
- CWE
- CWE-754
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Report tools
Action score
30
Monitor
Low priority for now.
- CVSS
- 30 / 40 · 7.5 / 10
- CISA KEV
- 0 / 30 · Not listed
- EPSS
- 0 / 30 · 0.6%
CNA vs NVD score
- NVD
- 7.5
- CNA · GitHub_M
- 5.0
- 2.5 point gap
CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:C/C:L/I:L/A:L
The score given by the assigning authority versus NVD’s independent score. A gap means the severity is contested.
Affected systems
| Vendor | Product | CPE |
|---|---|---|
| tensorflow | cpe:2.3:a:google:tensorflow |
Affected versions
NVD version ranges (for catalog products). Add the product to your stack with a version and matching uses these.
- google tensorflowbefore 1.15.2
- google tensorflow2.0.0 and later · before 2.0.1
Versions reported by the vendor
Affected version ranges reported by the assigning authority (GitHub_M). Independent of NVD's CPE analysis and usually ahead of it.
TensorFlow TensorFlow
- < 1.15.2, = 2.0.0affected
Package-level exposure
OSV and GitHub Advisory data: ecosystem, package and range. SBOM matching uses this table.
| Ecosystem | Package | Affected range | Fix |
|---|---|---|---|
| PyPI | tensorflow | from 2.0.0 · before 2.0.1 | 2.0.1 |
| PyPI | tensorflow | before 1.15.2 | 1.15.2 |
| PyPI | tensorflow-cpu | from 2.0.0 · before 2.0.1 | 2.0.1 |
| PyPI | tensorflow-cpu | before 1.15.2 | 1.15.2 |
| PyPI | tensorflow-gpu | from 2.0.0 · before 2.0.1 | 2.0.1 |
| PyPI | tensorflow-gpu | before 1.15.2 | 1.15.2 |
Same product
google: all recordsOther highest-scoring records for the same primary product.
- CVE-2023-25668TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation39Monitor
- CVE-2023-25664TensorFlow vulnerable to Heap Buffer Overflow in AvgPoolGrad39Monitor
- CVE-2022-41900FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow39Monitor
- CVE-2022-35939Out of bounds write in `scatter_nd` op in TensorFlow Lite39Monitor
- CVE-2022-23587Integer overflow in Tensorflow39Monitor
- CVE-2020-15208Data corruption in tensorflow-lite39Monitor
Remediation
Which version to upgrade to
Fix versions compiled from the vendor, package registries and Microsoft. Verify the vendor's note before upgrading.
| Product / package | Fixed version | Source |
|---|---|---|
| PyPI:tensorflow | 1.15.2 | Package registry (OSV) |
| PyPI:tensorflow | 2.0.1 | Package registry (OSV) |
| PyPI:tensorflow-cpu | 1.15.2 | Package registry (OSV) |
| PyPI:tensorflow-cpu | 2.0.1 | Package registry (OSV) |
| PyPI:tensorflow-gpu | 1.15.2 | Package registry (OSV) |
| PyPI:tensorflow-gpu | 2.0.1 | Package registry (OSV) |
Exploit status
No known public exploit
No public exploit has been observed yet. That doesn't mean you're safe, only that the bar is a little higher.
Research context
For pentesters and researchers: attack profile, score disagreement, timeline, patch commits, credits, variant and chain candidates, bug bounty scope. All derived from existing data; no exploit code.
Timeline
From publication to today: proof of concept, Metasploit module, CISA KEV and fix record. Dates are as reported by the sources.
No dated events beyond publication.
FIRST EPSS daily score; only changes of 0.01 or more are recorded (step chart).
Patch and commit links
Commit, PR and diff links among the references. A starting point for patch-diffing and variant hunting; fixes, not exploits.
Credits
All researchersFinders, reporters and analysts named in the CNA record. Click a name for that researcher’s other records.
No credits in the CNA record.
Variant candidates
Same product, same weakness class, within 18 months. If the patch missed the root cause, the sibling bug is here.
No nightly-computed relations.
Chain candidates
An authentication bypass and a privilege-requiring bug in the same product, published close together: combined they may become an unauthenticated path.
—
Bug bounty scope
No known public program.
Source: bounty-targets-data (public HackerOne, Bugcrowd, Intigriti, YesWeHack listings).
Technical details
Attack conditions
- Anyone who can reach it over the internet can trigger it.
- No account or password is required.
- No user action is required.
- No special conditions are required; it is repeatable.
If successful
- Confidentiality
- none
- Integrity
- none
- Availability
- high · the service can be disrupted
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality impact
- None
- Integrity impact
- None
- Availability impact
- High
Weakness class (CWE)
CWE-754 · Improper Check for Unusual or Exceptional ConditionsCVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
nvd-primary
Attack context
MITRE CAPEC attack patterns and ATT&CK techniques for this weakness class (CWE). A starting point for detection rules and threat hunting.
MITRE has no CAPEC/ATT&CK mapping for this CWE.
Change log
- Fix✗ → ✓
For records you follow, these changes also arrive as notifications. →
References
- github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf
- github.com/tensorflow/tensorflow/releases/tag/v1.15.2
- github.com/tensorflow/tensorflow/releases/tag/v2.0.1
Vendor advisories and official records. Exploit/PoC links are deliberately left out.