CVE-2025-5086 (GCVE-0-2025-5086)
Vulnerability from cvelistv5 – Published: 2025-06-02 17:42 – Updated: 2026-02-26 18:27Title
Deserialization of Untrusted Data vulnerability affecting DELMIA Apriso from Release 2020 through Release 2025
Summary
A deserialization of untrusted data vulnerability affecting DELMIA Apriso from Release 2020 through Release 2025 could lead to a remote code execution.
Severity
9 (Critical)
SSVC
Exploitation: active
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-09-12 03:56 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
References
3 references
| URL | Tags |
|---|---|
| https://www.3ds.com/vulnerability/advisories | |
| https://isc.sans.edu/diary/Exploit+Attempts+for+D… | media-coverage |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Dassault Systèmes | DELMIA Apriso |
Affected:
Release 2020 Golden , ≤ Release 2020 SP4
(custom)
Affected: Release 2021 Golden , ≤ Release 2021 SP3 (custom) Affected: Release 2022 Golden , ≤ Release 2022 SP3 (custom) Affected: Release 2023 Golden , ≤ Release 2023 SP3 (custom) Affected: Release 2024 Golden , ≤ Release 2024 SP1 (custom) Affected: Release 2025 Golden , ≤ Release 2025 SP1 (custom) |
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 9144fb5e-ea1f-45bf-ae10-f0c0c5c6df88
Exploited: Yes
Timestamps
First Seen: 2025-09-11
Asserted: 2025-09-11
Scope
Notes: Dassault Systèmes DELMIA Apriso Deserialization of Untrusted Data Vulnerability | Affected: Dassault Systèmes / DELMIA Apriso | Description: Dassault Systèmes DELMIA Apriso contains a deserialization of untrusted data vulnerability that could lead to a remote code execution. | Required action: Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable. | Due date: 2025-10-02 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://www.3ds.com/trust-center/security/security-advisories/cve-2025-5086 ; https://nvd.nist.gov/vuln/detail/CVE-2025-5086
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | CWE-502 |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | DELMIA Apriso |
| Due Date | 2025-10-02 |
| Date Added | 2025-09-11 |
| Vendorproject | Dassault Systèmes |
| Vulnerabilityname | Dassault Systèmes DELMIA Apriso Deserialization of Untrusted Data Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-10-02 08:09 CEST
| Updated: 2026-10-02 08:09 CEST
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: d24d016e-6156-4320-ad82-ff426fea7f80
Exploited: Yes
Timestamps
First Seen: 2026-06-01
Asserted: 2026-06-01
Scope
Notes: Deserialization of Untrusted Data vulnerability affecting DELMIA Apriso from Release 2020 through Release 2025 | Affected: Dassault Systèmes / DELMIA Apriso | CVSS: 9.0 (CRITICAL) | EPSS: 0.96915 | Used in malware: unknown | Listed 1 day ahead of CISA KEV | Not yet in CISA KEV: False
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | Deserialization of Untrusted Data vulnerability affecting DELMIA Apriso from Release 2020 through Release 2025 |
| Cve Id | CVE-2025-5086 |
| Vendor | Dassault Systèmes |
| Ghsa Id | None |
| Product | DELMIA Apriso |
| Added Date | 2026-06-01T10:40:27.301Z |
| Cvss Score | 9.0 |
| Epss Score | 0.96915 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99888 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2025-5086 |
| Ahead Of Cisa Kev |
|
| Not Yet In Cisa Kev | False |
References
Created: 2026-10-02 09:10 CEST
| Updated: 2026-10-02 09:10 CEST
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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