CVE-2024-36420 (GCVE-0-2024-36420)
Vulnerability from cvelistv5 – Published: 2024-07-01 15:53 – Updated: 2024-08-02 03:37Title
GHSL-2023-232: Flowise Path Injection at /api/v1/openai-assistants-file
Summary
Flowise is a drag & drop user interface to build a customized large language model flow. In version 1.4.3 of Flowise, the `/api/v1/openai-assistants-file` endpoint in `index.ts` is vulnerable to arbitrary file read due to lack of sanitization of the `fileName` body parameter. No known patches for this issue are available.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-01 20:59 UTC
CWE
- CWE-74 - Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://securitylab.github.com/advisories/GHSL-20… | x_refsource_CONFIRM |
| https://github.com/FlowiseAI/Flowise/blob/e93ce07… | x_refsource_MISC |
Impacted products
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 863e96d2-7d1c-4108-8e55-ed71b3ff2bb3
Exploited: Yes
Timestamps
First Seen: 2026-07-17
Asserted: 2026-07-17
Scope
Notes: GHSL-2023-232: Flowise Path Injection at /api/v1/openai-assistants-file | Affected: FlowiseAI / Flowise | CVSS: 7.5 (HIGH) | EPSS: 0.01776 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: kevintel
Details
| Feed | KEVIntel (kevintel.com) |
|---|---|
| Title | GHSL-2023-232: Flowise Path Injection at /api/v1/openai-assistants-file |
| Vendor | FlowiseAI |
| Product | Flowise |
| Added Date | 2026-07-17T08:33:53.505Z |
| Cvss Score | 7.5 |
| Epss Score | 0.01776 |
| Cvss Severity | HIGH |
| Epss Percentile | 0.75707 |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-07-17 09:00 UTC
| Updated: 2026-07-17 09:00 UTC
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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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