VulnerabilityPublished 10 Mar 2025Updated 29 Dec 2025

CVE-2025-1944: mmaitre314 picklescan · Insufficient Verification of Data Authenticity

Affects mmaitre314 picklescanSource CVE Database V5

Not known to be exploited · CVSS 5.3 Medium.

Data as of 26 Oct 2026, 12:55 UTC

CVSS 4.0 base score

5.3Medium

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

EPSS: 0.3% chance of exploitation in 30 days – higher than 25% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:L/VA:L/SC:N/SI:L/SA:L

Attack Vector: Network
Attack Complexity: Low
Attack Requirements: None
Privileges Required: None
User Interaction: Passive
Vulnerable system confidentiality: None
Vulnerable system integrity: Low
Vulnerable system availability: Low
Subsequent system confidentiality: None
Subsequent system integrity: Low
Subsequent system availability: Low

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

Attack Vector: Network: The vulnerable system is bound to the network stack and the set of possible attackers extends beyond the other options listed below, 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: The attacker must take no measurable action to exploit the vulnerability. The attack requires no target-specific circumvention to exploit the vulnerability. An attacker can expect repeatable success against the vulnerable system.
Attack Requirements: None: The successful attack does not depend on the deployment and execution conditions of the vulnerable system. The attacker can expect to be able to reach the vulnerability and execute the exploit under all or most instances of the vulnerability.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.
User Interaction: Passive: Successful exploitation of this vulnerability requires limited interaction by the targeted user with the vulnerable system and the attacker’s payload. These interactions would be considered involuntary and do not require that the user actively subvert protections built into the vulnerable system.
Vulnerable system confidentiality: None: There is no loss of confidentiality within the Vulnerable System.
Vulnerable system 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 to the Vulnerable System.
Vulnerable system 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 Vulnerable System 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 Vulnerable System.
Subsequent system confidentiality: None: There is no loss of confidentiality within the Subsequent System or all confidentiality impact is constrained to the Vulnerable System.
Subsequent system 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 to the Subsequent System.
Subsequent system 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 Subsequent System 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 Subsequent System.

Definitions: FIRST CVSS v4.0 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 10 Mar 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 26 Oct 2026.

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 or the CVSS exploit maturity 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
PyPIpicklescan`pkg:pypi/picklescan`Other threats	Affected =0.0.1	Fixed in–

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

AI-suggested

Immediately upgrade picklescan to version 0.0.23 or later once available, as the vulnerability affects versions before this.
Until a patch is released, implement additional validation layers for PyTorch model archives, such as verifying archive integrity with trusted cryptographic hashes or signatures before scanning.
Employ sandboxing or isolated environments to load and test PyTorch models before deployment to contain potential malicious payloads.
Monitor AI/ML pipelines for anomalous behavior or crashes that could indicate exploitation attempts.
Educate developers and data scientists about the risks of loading untrusted or unauthenticated PyTorch models.
Consider alternative scanning tools or manual code review for critical models if picklescan cannot be updated promptly.
Integrate multi-factor verification for model provenance and supply chain security to reduce the risk of malicious model insertion.
Collaborate with PyTorch and picklescan maintainers to track vulnerability disclosures and patches.

Description

picklescan before 0.0.23 is vulnerable to a ZIP archive manipulation attack that causes it to crash when attempting to extract and scan PyTorch model archives. By modifying the filename in the ZIP header while keeping the original filename in the directory listing, an attacker can make PickleScan raise a BadZipFile error. However, PyTorch's more forgiving ZIP implementation still allows the model to be loaded, enabling malicious payloads to bypass detection.

Impact

For European organizations, this vulnerability could undermine the security of machine learning workflows that incorporate PyTorch models scanned by picklescan. Attackers could bypass detection and introduce malicious code into AI/ML pipelines, potentially leading to unauthorized data access, model manipulation, or disruption of AI services. This can affect confidentiality by exposing sensitive data processed by models, integrity by corrupting model outputs or training data, and availability by causing crashes or denial of service in AI systems. Industries relying heavily on AI, such as finance, healthcare, automotive, and critical infrastructure, may face operational and reputational risks. The medium CVSS score reflects moderate impact, but the lack of authentication and ease of exploitation via crafted archives increases the threat surface. Since no known exploits are reported, the immediate risk is moderate but could escalate if weaponized. Organizations using picklescan in their ML security toolchain must be vigilant to prevent supply chain attacks or adversarial model insertions.

CWE-345Insufficient Verification of Data Authenticity (opens in a new tab)
Class weaknessOWASP Top 10 A08:2025: Software or Data Integrity Failures (opens in a new tab)
The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data.
MITRE discourages mapping vulnerabilities to this broad entry; the root cause is usually a more specific weakness.
OWASP Cheat Sheets
XML Security (opens in a new tab)
REST Security (opens in a new tab)
JSON Web Token for Java (opens in a new tab)
REST Assessment (opens in a new tab)
Cross-Site Request Forgery Prevention (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.2 (opens in a new tab)
V3.5.3 (opens in a new tab)
V13.2.6 (opens in a new tab)
Attack patterns
JSON Hijacking (aka JavaScript Hijacking) (opens in a new tab)
Cache Poisoning (opens in a new tab)
DNS Cache Poisoning (opens in a new tab)
Spoofing of UDDI/ebXML Messages (opens in a new tab)
Application API Message Manipulation via Man-in-the-Middle (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 30 Dec 2025, 23:51 UTC

WeaknessesCWE-345

Technical details

References

Community reviews

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