CVE-2025-0868 (GCVE-0-2025-0868)
Vulnerability from cvelistv5 – Published: 2025-02-20 11:26 – Updated: 2025-10-03 08:56Title
Remote Code Execution in DocsGPT
Summary
A vulnerability, that could result in Remote Code Execution (RCE), has been found in DocsGPT. Due to improper parsing of JSON data using eval() an unauthorized attacker could send arbitrary Python code to be executed via /api/remote endpoint..
This issue affects DocsGPT: from 0.8.1 through 0.12.0.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-20 14:15 UTC
CWE
- CWE-95 - Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://cert.pl/en/posts/2025/02/CVE-2025-0868/ | third-party-advisory |
| https://cert.pl/posts/2025/02/CVE-2025-0868/ | third-party-advisory |
| https://github.com/arc53/DocsGPT | product |
Impacted products
Date Public
2025-02-20 11:00
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 017603e2-25ad-46d2-b1dd-aeded67e1e85
Exploited: Yes
Timestamps
First Seen: 2025-06-21
Asserted: 2025-06-21
Scope
Notes: Remote Code Execution in DocsGPT | Affected: Arc53 / DocsGPT | CVSS: 9.3 (CRITICAL) | EPSS: 0.17087 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | Remote Code Execution in DocsGPT |
| Cve Id | CVE-2025-0868 |
| Vendor | Arc53 |
| Ghsa Id | GHSA-9GFF-5V8W-X922 |
| Product | DocsGPT |
| Added Date | 2025-06-21T00:00:00.000Z |
| Cvss Score | 9.3 |
| Epss Score | 0.17087 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.96992 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2025-0868 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:09 CEST
| Updated: 2026-10-02 09:09 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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