CVE-2026-51886 (GCVE-0-2026-51886)
Vulnerability from cvelistv5 – Published: 2026-10-01 00:00 – Updated: 2026-10-05 23:56Summary
langflow-ai langflow v1.9.3 is affected by: Code Injection. The impact is: execute arbitrary code (remote). The component is: src/backend/base/langflow/api/v1/validate.py:validate-post_validate_code-a-real-authenticated-http-post-to-api-v1. The attack vector is: Attack surface: HTTP or browser-backed service path. A public-facing route accepts raw Python source and forwards it into a server-side compile/exec validation path without any visible entitlement guard. ¶¶ A weakness has been identified in langflow-ai langflow up to 1.9.3. langflow contains a code injection vulnerability in validate-post_validate_code-a-real-authenticated-http-post-to-api-v1 (src/backend/base/langflow/api/v1/validate.py:13). An authenticated attacker can execute arbitrary Python code on the server by submitting malicious code to the /api/v1/validate/code endpoint, which directly executes user-supplied code without sandboxing or security controls.
Severity
9.8 (Critical)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-05 23:56 UTC
CWE
- n/a
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 3705cd39-ccb9-41e1-b34c-ad0c8aabf03b
Exploited: Yes
Timestamps
First Seen: 2026-10-06
Asserted: 2026-10-06
Scope
Notes: langflow-ai langflow v1.9.3 is affected by: Code Injection. The impact is: execute arbitrary code (remote). The component is:... | Affected: Langflow-ai / langflow | CVSS: 9.8 (CRITICAL) | EPSS: 0.00403 | 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 | langflow-ai langflow v1.9.3 is affected by: Code Injection. The impact is: execute arbitrary code (remote). The component is:... |
| Cve Id | CVE-2026-51886 |
| Vendor | Langflow-ai |
| Ghsa Id | None |
| Product | langflow |
| Added Date | 2026-10-06T10:31:37.276Z |
| Cvss Score | 9.8 |
| Epss Score | 0.00403 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.32379 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2026-51886 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-07 11:01 UTC
| Updated: 2026-10-07 11:01 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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