Detecting and responding to CVE-2025-3248, the Langflow unauthenticated RCE

CVE-2025-3248 is a remote, unauthenticated code-execution flaw in Langflow, the open-source framework for building agent and retrieval pipelines. It carries a CVSS base score of 9.8, it sits on the CISA Known Exploited Vulnerabilities catalogue, and it has been exploited in the wild. A single crafted HTTP request to one API endpoint runs attacker-chosen code on the host, with no login required.

This is a defender's guide. It does not walk through the exploit and it carries no working payload. The aim is narrower and more useful to a blue team: know what exploitation of this flaw looks like in your logs, hunt for attempts that already happened, confirm whether a host was actually touched, and close the door. Everything below is a detection or a response step, grounded in the vendor advisory and the primary CVE record.

Two facts frame the whole piece. The flaw is trivial to trigger and needs no credentials, so exposure equals risk. And the patch is not new: Langflow 1.3.0, which fixes it, shipped on 31 March 2025. Any instance still running an earlier build, reachable from an untrusted network, should be treated as a live target, and if it has been exposed for any length of time, as a possible incident.

The flaw, in just enough detail to detect it

Langflow exposed an endpoint, /api/v1/validate/code, that accepted a block of Python and validated it before a flow used it. Validation did not merely parse the code; during the check it compiled and executed parts of what it was handed. Because the endpoint required no authentication, anyone who could reach the service could submit code that ran on the server. The vendor fix in 1.3.0 places the endpoint behind the authenticated current user.

You do not need the exploit mechanics to defend against this, only the shape of it: an unauthenticated POST to /api/v1/validate/code carrying Python in a JSON body, after which the Langflow process may do something a code-validation endpoint should never do, such as spawn a shell or open an outbound connection. That one sentence drives every detection below.

What exploitation looks like in telemetry

Exploitation shows up in three places at once, and correlating them is what turns a noisy signal into a finding. The first is the web tier: a POST to the validation endpoint. The second is the host: the Langflow server process spawning a child it has no business spawning. The third is the network: an outbound connection from the app host to somewhere unfamiliar, moments after the request.

Start with the request. On any reverse proxy or web server in front of Langflow, the raw request has a fixed, alertable shape, a method, a path, and a content type. The body varies and you do not need to inspect it to alert on the pattern.

POST /api/v1/validate/code HTTP/1.1
Host: langflow.lab.example
Content-Type: application/json
Content-Length: <n>

{"code": "<python submitted for validation>"}

In access logs the same event is one line. Surface every hit, then triage by source address and whether the caller was authenticated. Legitimate validation traffic comes from your own users through the interface, not from strangers on the internet.

# combined-format access log entry; source is a documentation-range address
198.51.100.23 - - [09/Apr/2025:14:22:07 +0000] "POST /api/v1/validate/code HTTP/1.1" 200 512 "-" "python-requests/2.31.0"

# surface every request to the endpoint and rank by source address
grep -E '"POST /api/v1/validate/code' access.log \
  | awk '{print $1, $9}' | sort | uniq -c | sort -rn

A 200 here is not proof of compromise; the endpoint answers legitimate validation calls too. What raises it from noise to incident is context: the caller is external or unauthenticated, the user agent is a scripting library rather than a browser, or the same request is immediately followed by a child process or an outbound connection on the host. Those pairings are the subject of the next section.

Detection rules

Two Sigma rules cover the two strongest signals. The first watches the web tier for the request itself; keep it at a modest severity because the endpoint has legitimate callers. The second watches the host for the Langflow process spawning a command interpreter, which is high-fidelity: a validation service has no reason to launch a shell.

title: Langflow unauthenticated code-validation request (CVE-2025-3248)
id: 8b6b0b2a-3d4e-4c21-9d2a-2f1c9a0e4d10
status: experimental
description: >
  POST requests to Langflow's /api/v1/validate/code endpoint, which is
  unauthenticated and executes submitted Python in versions before 1.3.0.
references:
  - https://nvd.nist.gov/vuln/detail/CVE-2025-3248
logsource:
  category: webserver
detection:
  selection:
    cs-method: POST
    cs-uri-stem|endswith: /api/v1/validate/code
  condition: selection
falsepositives:
  - Authenticated users validating code components through the Langflow UI
level: medium

title: Langflow server spawns shell or downloader (post-exploitation, CVE-2025-3248)
id: 1c9f7e64-2a55-4f0b-8f3d-6b7a0d4e2c11
status: experimental
description: >
  The Langflow python/uvicorn process spawning a shell or network tool,
  consistent with code execution through the validate/code endpoint.
logsource:
  category: process_creation
  product: linux
detection:
  parent:
    ParentImage|endswith:
      - '/python'
      - '/python3'
      - '/uvicorn'
  child:
    Image|endswith:
      - '/sh'
      - '/bash'
      - '/dash'
      - '/nc'
      - '/curl'
      - '/wget'
  condition: parent and child
falsepositives:
  - Startup scripts or components that legitimately shell out
level: high

Tune the parent list to how you run Langflow. If it lives in a container the parent image and child paths differ, but the logic holds: the application process should not be the parent of a shell, curl, wget, or nc. Pair a firing of the host rule with a matching request in the web rule inside a short window and you have a near-certain detection.

Threat hunting and indicators of compromise

Detections catch what happens next; hunting finds what already happened. Because public exploit code has circulated since April 2025 and honeypots logged attempts soon after, an instance that was exposed before you patched deserves a look back through retained logs, not just forward-looking alerts.

The first hunt is over web access logs: every historical POST to the endpoint, grouped by source, with first and last seen. Unfamiliar external addresses, scripting user agents, and bursts of requests are the rows to pull on.

index=web sourcetype=access_combined
  uri_path="/api/v1/validate/code" http_method=POST
| stats count,
        values(status)  as statuses,
        earliest(_time) as first_seen,
        latest(_time)   as last_seen
  by src_ip, http_user_agent
| sort - count

Run the equivalent over process-creation events for any child of the Langflow process, and over network telemetry for outbound connections from the app host that do not match its normal egress. When those three lists share a timestamp you have found an intrusion, not an attempt. Sweep the host for the artefacts below.

Confirming, preserving, and containing

If a hunt turns up a match, move from detection to response. The goal of the first hour is to answer one question, was code actually executed on this host, and to preserve the evidence that answers it before anything is rebuilt.

Langflow contains a missing authentication vulnerability in the /api/v1/validate/code endpoint that allows a remote, unauthenticated attacker to execute arbitrary code via crafted HTTP requests. CISA Known Exploited Vulnerabilities Catalog, CVE-2025-3248

Confirmation comes from correlation, not from a single log. A POST to the endpoint on its own is an attempt. The same request lined up in time with a child process spawned by the Langflow server, or with an outbound connection from the host, is confirmation that the request ran code.

Remediation, and the lesson under it

Remediation is short. Upgrade to Langflow 1.3.0 or later, which authenticates the validation endpoint. Do not stop at the upgrade: put the service behind an authenticating reverse proxy or a VPN, restrict who can reach it on the network, and constrain outbound traffic from the host so a future code-execution flaw cannot phone home as freely.

The lasting lesson is a design one. This flaw existed because a validation routine executed the thing it was meant to validate. Checking whether code is well-formed is a parsing and static-analysis problem; it never requires running the code, and running it hands control to whoever wrote it. Validate inputs by inspecting them, keep any real execution behind authentication and a sandbox, and an endpoint like this one cannot become an unauthenticated shell.

Sources

• NVD — CVE-2025-3248 detail (CVSS 9.8, CWE-94/CWE-306, affected and fixed versions)
• CISA Known Exploited Vulnerabilities Catalog
• Horizon3.ai — Unsafe at Any Speed: unauthenticated RCE in Langflow AI
• Langflow 1.3.0 release notes (released 31 March 2025)
• Langflow PR 6911 — authenticate current user on code validation (the fix)
• The Hacker News — Critical Langflow flaw added to CISA KEV amid ongoing exploitation