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GHSA-rvqx-wpfh-mfx7 CVE-2025-3248 PYSEC-2025-36 PYSEC-2026-380

Langflow Unauth RCE

Critical Known exploited PyPI
Severity
9.3 / 10
Critical · CVSS 4.0
Exploit chance (EPSS)
99.99%
Chance of exploitation in the next 30 days. Higher than 99.99% of scored vulnerabilities.
Known exploited
Yes
In CISA's catalog since May 5, 2025
Weakness
CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix
Available
2 packages have a fixed version
Exploited in the wild. Langflow Missing Authentication Vulnerability (Langflow Langflow). Required action: Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable. Due for U.S. federal agencies by May 26, 2025. Known to be used in ransomware campaigns.

Description

Langflow versions prior to 1.3.0 are susceptible to code injection in the /api/v1/validate/code endpoint. A remote and unauthenticated attacker can send crafted HTTP requests to execute arbitrary code.

Affected versions

PyPI

PackageVulnerableFixed in
langflow
< 1.3.0
1.3.0
langflow
< 1.3.0
1.3.0
langflow-base
< 0.3.0
0.3.0
langflow-base
< 0.3.0
0.3.0

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

langflow PyPI → 1.3.0

pip
pip install --upgrade "langflow>=1.3.0"
poetry
poetry add "langflow>=1.3.0"
uv
uv add "langflow>=1.3.0"

langflow-base PyPI → 0.3.0

pip
pip install --upgrade "langflow-base>=0.3.0"
poetry
poetry add "langflow-base>=0.3.0"
uv
uv add "langflow-base>=0.3.0"

Severity breakdown

CVSS 4.0 9.3

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:A

Attack vector
Network
Attack complexity
Low
Attack requirements
None
Privileges required
None
User interaction
None
Confidentiality (vulnerable system)
High
Integrity (vulnerable system)
High
Availability (vulnerable system)
High
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None
Exploit maturity
Attacked

CVSS 3.1 9.8

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it
  • Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
  • Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
  • 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, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
  • Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
  • Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Timeline

  1. Published
  2. Added to CISA's Known Exploited Vulnerabilities catalog
  3. Remediation due for U.S. federal agencies
  4. Last updated by the source
  5. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-vwmf-pq79-vjvx
Unauthenticated Remote Code Execution in Langflow via Public Flow Build Endpoint
Critical Known exploited Linked by the source
GHSA-qrpv-q767-xqq2
Langflow: IDOR Vulnerability in `/api/v1/responses` Endpoint Allows Authenticated Attackers to Access Another User's Flow
High Known exploited Also affects langflow
GHSA-g22f-v6f7-2hrh
Langflow affected by Remote Code Execution via validate_code() exec()
High Known exploited Also affects langflow
GHSA-577h-p2hh-v4mv
Langflow CORS misconfiguration enables Account Takeover and RCE
Critical Known exploited Also affects langflow
GHSA-j8f7-x8jm-wmm4
Langflow: SSRF in URL-taking components (protection disabled by default / warn-only, not applied to RSS, SearXNG, Web Search, Home Assistant, Glean, Docling)
Medium Also affects langflow
GHSA-4f6c-2vvp-gw82
Langflow: IP Spoofing Bypass via `X-Forwarded-For` Allowing Remote Configuration Write
High Also affects langflow
GHSA-4hmc-cfm3-w43c
Langflow has Authenticated Cross-Project File Disclosure via Unscoped MCP Resource Handlers
High Also affects langflow
GHSA-gh98-4phw-6fmw
Langflow : Cross-user flow access and vertex execution via deprecated /api/v1/build/{flow_id}/vertices endpoints
Medium Also affects langflow

Credits: chximn-dt

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