CVE-2024-0960VulnerabilityPublished 27 Jan 2024Updated 2 Jun 2025

CVE-2024-0960: flink-extended ai-flow · Deserialization

Affects flink-extended ai-flowSource CVE Database V5

Not known to be exploited · CVSS 5.0 Medium.

Data as of 26 Oct 2026, 07:57 UTC

CVSS 3.1 base score

5.0Medium

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

EPSS: 0.7% chance of exploitation in 30 days – higher than 53% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

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

Attack Vector: Network
Attack Complexity: High
Privileges Required: None
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: High: A successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected. For example, a successful attack may require an attacker to: gather knowledge about the environment in which the vulnerable target/component exists; prepare the target environment to improve exploit reliability; or inject themselves into the logical network path between the target and the resource requested by the victim in order to read and/or modify network communications (e.g., a man in the middle attack).
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
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 27 Jan 2024
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

Affected software

Product	Affected	Fixed in
PyPIai-flow`pkg:pypi/ai-flow`Other threats	Affected =0.3.1	Fixed in–

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AI-suggested

1. Immediate mitigation should include restricting network access to ai-flow services, limiting exposure to trusted internal networks only. 2. Implement strict input validation and sanitization on any data that is deserialized by cloudpickle.loads to prevent untrusted data from being processed. 3. Monitor and log all deserialization activities and unusual behaviors in ai-flow workflows to detect potential exploitation attempts. 4. Employ application-layer firewalls or runtime application self-protection (RASP) solutions to detect and block malicious deserialization payloads. 5. 6. Educate users about the risk of interacting with untrusted inputs that could trigger deserialization attacks. 7. Isolate AI workflow environments to minimize the impact of a potential compromise. 8. Regularly review and audit the use of serialization/deserialization functions in the codebase to identify and remediate unsafe practices.

Description

A vulnerability was found in flink-extended ai-flow 0.3.1. It has been declared as critical. Affected by this vulnerability is the function cloudpickle.loads of the file \ai_flow\cli\commands\workflow_command.py. The manipulation leads to deserialization. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The identifier VDB-252205 was assigned to this vulnerability.

Impact

For European organizations utilizing flink-extended ai-flow version 0.3.1, this vulnerability poses a moderate risk. AI and data-driven enterprises relying on ai-flow for orchestrating machine learning workflows could face disruptions or data manipulation if exploited. Although the attack complexity is high and user interaction is required, the remote attack vector means that exposed services accepting untrusted input could be targeted by attackers. Confidentiality, integrity, and availability impacts are rated low individually, but combined they could affect the reliability of AI workflows, potentially leading to incorrect AI model training or deployment decisions. This could have downstream effects on business operations, especially in sectors like finance, healthcare, and manufacturing where AI workflows are critical. The lack of available patches increases the window of exposure. Organizations with exposed ai-flow instances should consider this vulnerability seriously to avoid potential operational disruptions or data integrity issues.

CWE-502Deserialization of Untrusted Data (opens in a new tab)
Base weaknessLikelihood of exploit: MediumCWE Top 25 (2025): #15OWASP Top 10 A08:2025: Software or Data Integrity Failures (opens in a new tab)
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
Architecture and Design · Implementation
If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Implementation
When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Implementation
Explicitly define a final object() to prevent deserialization.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
Deserialization (opens in a new tab)
XML Security (opens in a new tab)
XML External Entity Prevention (opens in a new tab)
Software Supply Chain Security (opens in a new tab)
OWASP ASVS 4.0.3
V1.5.2 (opens in a new tab)
V5.5.3 (opens in a new tab)
V5.5.1 (opens in a new tab)
Attack patterns
Object Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 3 Jul 2025, 18:11 UTC

WeaknessesCWE-502

Technical details

References

Community reviews

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