CVE-2026-33497 is an unauthenticated path traversal in Langflow that exposes the application's secret_key — the cryptographic root used to sign sessions and tokens. Any attacker with network access can read it in a single HTTP request, then forge authenticated sessions and take full control of the platform. Patch to 1.7.1 immediately and rotate your secret_key; if you cannot patch today, take the Langflow instance off the network until you do.
What is the risk?
HIGH. CVSS 7.5 understates operational risk for AI teams. The exploitability bar is trivial: no authentication, no special tooling, one HTTP GET with a path traversal payload. The impact ceiling is a full Langflow takeover — an attacker with a forged admin session inherits every connected LLM API key, database credential, and workflow secret stored in the platform. Langflow instances are frequently internet-exposed and used in production AI pipelines, making this a high-probability, high-impact target.
What systems are affected?
| Package | Ecosystem | Vulnerable Range | Patched |
|---|---|---|---|
| Langflow | pip | < 1.7.1 | 1.7.1 |
Do you use Langflow? You're affected.
How severe is it?
What is the attack surface?
What should I do?
6 steps-
PATCH
Upgrade to Langflow 1.7.1 immediately — this is the only complete fix.
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ROTATE
After patching, regenerate the secret_key and invalidate all existing sessions.
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NETWORK ISOLATION
Until patched, restrict Langflow to internal networks or VPN; do not expose port 7860 (or any Langflow port) to the internet.
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REVOKE CREDENTIALS
Audit all API keys stored in Langflow workflows and rotate any that could have been exposed during the vulnerability window.
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DETECT
Search web server logs for path traversal patterns in requests to /profile_pictures/ (e.g., %2e%2e, ../, .%2f). Presence indicates prior exploitation attempts.
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AUDIT
Review Langflow access logs for unexpected admin sessions or workflow modifications.
What does CISA's SSVC say?
Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.
How is it classified?
Which compliance frameworks are affected?
This CVE is relevant to:
Frequently Asked Questions
What is CVE-2026-33497?
CVE-2026-33497 is an unauthenticated path traversal in Langflow that exposes the application's secret_key — the cryptographic root used to sign sessions and tokens. Any attacker with network access can read it in a single HTTP request, then forge authenticated sessions and take full control of the platform. Patch to 1.7.1 immediately and rotate your secret_key; if you cannot patch today, take the Langflow instance off the network until you do.
Is CVE-2026-33497 actively exploited?
No confirmed active exploitation of CVE-2026-33497 has been reported, but organizations should still patch proactively.
How to fix CVE-2026-33497?
1. PATCH: Upgrade to Langflow 1.7.1 immediately — this is the only complete fix. 2. ROTATE: After patching, regenerate the secret_key and invalidate all existing sessions. 3. NETWORK ISOLATION: Until patched, restrict Langflow to internal networks or VPN; do not expose port 7860 (or any Langflow port) to the internet. 4. REVOKE CREDENTIALS: Audit all API keys stored in Langflow workflows and rotate any that could have been exposed during the vulnerability window. 5. DETECT: Search web server logs for path traversal patterns in requests to /profile_pictures/ (e.g., %2e%2e, ../, .%2f). Presence indicates prior exploitation attempts. 6. AUDIT: Review Langflow access logs for unexpected admin sessions or workflow modifications.
What systems are affected by CVE-2026-33497?
This vulnerability affects the following AI/ML architecture patterns: agent frameworks, LLM workflow builders, model serving, RAG pipelines, multi-agent orchestration.
What is the CVSS score for CVE-2026-33497?
CVE-2026-33497 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 7.99%.
What is the AI security impact?
Affected AI Architectures
MITRE ATLAS Techniques
AML.T0025 Exfiltration via Cyber Means AML.T0049 Exploit Public-Facing Application AML.T0055 Unsecured Credentials AML.T0083 Credentials from AI Agent Configuration AML.T0084 Discover AI Agent Configuration Compliance Controls Affected
What are the technical details?
Original Advisory
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.
Exploitation Scenario
An unauthenticated external attacker scans for Langflow instances on the internet (banner/version fingerprinting via the UI or API). They send a crafted GET request to /profile_pictures/../../app/secret_key or a similar traversal payload targeting the application's configuration directory. The server returns the secret_key value in the response body. The attacker uses the secret_key to forge a valid JWT session token with admin privileges and authenticates to the Langflow UI. From admin access, they export all workflow definitions (containing hardcoded LLM API keys), implant malicious prompt injections in shared workflow templates, and use Langflow's tool-calling capabilities to exfiltrate data from connected databases — all without ever needing to know a password.
Weaknesses (CWE)
CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Primary
CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Primary
CWE-22 — Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal'): The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.
- [Implementation] Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylis
- [Architecture and Design] For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
Source: MITRE CWE corpus.
CVSS Vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N References
Timeline
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