CVE-2024-31584VulnerabilityPublished 4 Jun 2025

CVE-2024-31584: pytorch · n/a

Affects pytorchSource CVE Database V5

Not known to be exploited · CVSS 5.5 Medium : upgrade to 2.2.0 when convenient.

Data as of 26 Oct 2026, 07:57 UTC

CVSS 3.1 base score

5.5Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.3% chance of exploitation in 30 days – higher than 32% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 2.2.0

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

Attack Vector: Network
Attack Complexity: Low
Privileges Required: Low
User Interaction: Required
Scope: Unchanged
Confidentiality: Low
Integrity: Low
Availability: Low

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: Low: The attacker is authorized with (i.e., requires) privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with Low privileges may have the ability to cause an impact only to non-sensitive resources.
User Interaction: Required: Successful exploitation of this vulnerability requires a user to take some action before the vulnerability can be exploited.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the impacted component.
Integrity: Low: Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact on the impacted component.
Availability: Low: Performance is reduced or there are interruptions in resource availability. Even if repeated exploitation of the vulnerability is possible, the attacker does not have the ability to completely deny service to legitimate users. The resources in the impacted component are either partially available all of the time, or fully available only some of the time, but overall there is no direct, serious consequence to the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 4 Jun 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Upgrade pytorch/pytorch to 2.2.0.

Affected software

Product	Affected	Fixed in
GitHubpytorch/pytorch`pkg:github/pytorch/pytorch`Other threats	Affected <2.2.0	Fixed in 2.2.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

1. Upgrade to PyTorch version 2.2.0 or later as soon as the patch becomes available to eliminate the vulnerability. 2. If you cannot patch yet, restrict network access to systems running vulnerable PyTorch versions, especially those exposing JIT mobile runtime components. 3. Implement strict input validation and sanitization on any data fed into the JIT mobile flatbuffer loader to minimize malformed input risks. 4. Monitor application logs and system behavior for unusual crashes or memory access errors that could indicate attempted exploitation. 5. Employ runtime protections such as memory-safe languages or sandboxing around PyTorch components to limit the impact of out-of-bounds reads. 6. Educate users and developers about the risk of social engineering attacks that might trigger user interaction required for exploitation. 7. Conduct regular security assessments of AI/ML deployment environments to identify and remediate similar memory safety issues proactively. 8. Use network segmentation to isolate AI/ML infrastructure from general user networks to reduce exposure.

Description

Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp.

Impact

The primary impact of CVE-2024-31584 is unauthorized reading of memory beyond allocated buffers, which can lead to partial disclosure of sensitive information residing in adjacent memory areas. This can compromise confidentiality by leaking data that should remain protected. Integrity impact is limited but possible if the out-of-bounds read leads to application instability or crashes, potentially disrupting normal operations. Availability could be affected if the vulnerability causes application crashes or denial of service conditions. Since exploitation requires low privileges and user interaction, attackers might leverage social engineering or trick users into triggering the vulnerability remotely. Organizations deploying PyTorch in production environments, especially those running JIT-compiled models on mobile or embedded platforms, could face risks of data leakage or service disruption. Failure to address this vulnerability could undermine trust in AI applications and expose sensitive model or data information.

CWE-125Out-of-bounds Read (opens in a new tab)
Base weaknessCWE Top 25 (2025): #8
The product reads data past the end, or before the beginning, of the intended buffer.
Implementation · Input Validation
Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
Architecture and Design · Language Selection
Use a language that provides appropriate memory abstractions.
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Overread Buffers (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 26 Feb 2026, 12:25 UTC

WeaknessesCWE-125

Technical details

References

Community reviews

Related threats