CVE-2026-33497 (GCVE-0-2026-33497)
Vulnerability from cvelistv5 – Published: 2026-03-24 13:14 – Updated: 2026-03-24 17:45Title
Langflow: /profile_pictures/{folder_name}/{file_name} endpoint file reading
Summary
Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.7.1, in the download_profile_picture function of the /profile_pictures/{folder_name}/{file_name} endpoint, the folder_name and file_name parameters are not strictly filtered, which allows the secret_key to be read across directories. Version 1.7.1 contains a patch.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-03-24 17:45 UTC
CWE
- CWE-22 - Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/langflow-ai/langflow/security/… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| langflow-ai | langflow |
Affected:
< 1.7.1
|
guessed |
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 4c0d1f4c-4339-4ad0-bfc9-2502056915b3
Exploited: Yes
Timestamps
First Seen: 2026-08-11
Asserted: 2026-08-11
Scope
Notes: Langflow: /profile_pictures/{folder_name}/{file_name} endpoint file reading | Affected: langflow-ai / langflow | CVSS: 8.7 (HIGH) | EPSS: 0.19584 | 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 | Langflow: /profile_pictures/{folder_name}/{file_name} endpoint file reading |
| Vendor | langflow-ai |
| Product | langflow |
| Added Date | 2026-08-11T12:08:51.170Z |
| Cvss Score | 8.7 |
| Epss Score | 0.19584 |
| Cvss Severity | HIGH |
| Epss Percentile | 0.97122 |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-08-11 13:00 UTC
| Updated: 2026-08-11 13:00 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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