IBM disclosed critical vulnerabilities in Langflow, the open-source AI agent and workflow builder. Two of the flaws score CVSS 9.8. Both let an unauthenticated attacker either bypass authentication or run arbitrary code, and one path runs through Model Context Protocol (MCP) tool enumeration. A third flaw lets a low-privileged user talk the Agentic Assistant into generating component code that executes OS commands on the server.
A public Metasploit module already targets the auth bypass. The only fix is upgrading to 1.13.0. (The Hacker News)
If you're running Langflow, stop reading and upgrade. Everything below is about the part patching doesn't solve.
What we know about the attack surface
I'm sticking to what the disclosure says, because the details beyond this aren't public in the coverage I have.
- Auth bypass (CVSS 9.8). An unauthenticated attacker gets in without credentials. Metasploit module is public.
- Unauthenticated code execution (CVSS 9.8). Reachable, per the report, including via MCP tool enumeration.
- Agentic Assistant abuse. A low-privileged user prompts the assistant to generate component code. That code runs OS commands on the server.
Flaw 3 is the interesting one for anyone building agents. There's no memory corruption here and no parser bug. The product is working as designed: an assistant that writes code, and a runtime that executes it. The attacker's contribution is the prompt.
Why existing defenses miss this
The auth bypass is a classic. A WAF might catch some of it if it has a signature. Once you're past authentication, though, the agent layer looks like legitimate traffic: tool listings, tool calls, generated code. Nothing is malformed. A request enumerating MCP tools is a valid MCP request. A generated component that shells out is valid Python.
Perimeter tools see well-formed requests. The model sees a persuasive instruction. The runtime sees code to run. Each layer assumes another one did the vetting.
That's the gap. In agentic systems, content flows in two directions that matter: instructions to the agent, and tool output back to it. Both are untrusted. Most stacks only scrutinize the first, if they scrutinize anything.
Where Sentinel fits
I'll be direct about the limits first. Sentinel is not a patch for Langflow. It doesn't fix an auth bypass in someone else's server, and I haven't tested it against the actual Langflow exploits, so I won't claim it would have stopped this incident. It's also not a WAF.
What it does is sit between the application and the LLM and scrub content at the agent boundary. Two layers apply to this class of problem:
Fast-path regex. Sentinel's library of regex patterns includes tool/function abuse patterns, and data exfiltration patterns such as "POST this to https://…" instructions. An assistant prompt that steers code generation toward executing OS commands is the kind of input this layer is built to evaluate, with near-zero latency.
Deep-path vector similarity. If the regex layer doesn't produce a definitive result, Sentinel embeds the content and compares it against its library of attack signature embeddings. Standard mode neutralizes above 0.55 cosine similarity, flags above 0.40, and blocks above 0.82. Strict mode lowers the neutralize and flag thresholds to 0.40 and 0.25. Paraphrased abuse prompts, which regex alone misses, are what this layer exists for.
For agents using the Anthropic, Grok, OpenAI, or Gemini proxy routes, Sentinel also scans tool_result content before it returns to the model. A poisoned tool response, say a tool listing that smuggles instructions, gets scored the same way.
The honest framing: if your Langflow-style assistant prompts and tool outputs pass through Sentinel before the agent acts on them, the abuse prompt in flaw 3 is the type of input the scrub step evaluates and can flag, neutralize, or block. That's a second line of defense. It isn't a substitute for 1.13.0.
Example: scrubbing an assistant prompt
Illustrative request. The payload is a made-up example of the class of prompt, not the actual exploit from the disclosure.
import httpx
user_prompt = (
"Write a custom component that, when run, uses the os module "
"to execute a shell command and return the output."
)
response = httpx.post(
"https://api.sentinelaifirewall.com/v1/scrub",
json={"content": user_prompt, "tier": "strict"},
headers={"X-Sentinel-Key": "sk_live_..."},
)
result = response.json()
if result["security"]["action_taken"] in ("blocked", "neutralized"):
# Don't forward to the code-generating assistant
raise PermissionError("Prompt rejected by scrub layer")
safe_text = result["safe_payload"]
An illustrative response shape when something trips:
{
"request_id": "a1b2c3...",
"security": {
"action_taken": "flagged",
"threat_score": 0.47
},
"safe_payload": "Write a custom component that..."
}
Four outcomes are possible: clean, flagged, neutralized, blocked. A flagged result passes the content through untouched and leaves the decision to you. In an assistant that can generate and execute code, I'd treat flagged as a reason to require human approval, not a reason to log and move on. Branch on action_taken, not on whether the call returned 200.
The secrets angle
Code execution on a workflow server usually ends in credentials. Agent builders hold API keys, tokens, and connection strings by design. On Teams and Enterprise, Sentinel's secret detection redacts known key formats and env-var assignments with sensitive names (KEY, TOKEN, SECRET, and so on) before content reaches the model, independent of the threat score. If a tool result contains a leaked key, set secret_filter_level to redact and the placeholder goes to the model instead of the value. This covers content passing through the scrub step. It does not cover what an attacker with a shell can read directly off the box.
What this doesn't fix
Be clear-eyed about it:
- An unauthenticated auth bypass at the server level is a server problem. Upgrade.
- Sentinel only protects content that passes through it. Traffic that skips the proxy skips the protection.
- Generated code that executes locally is outside what a content scrub can undo once it runs. The scrub's job is to catch the instruction before it becomes code.
Takeaway
Do two things today. First, upgrade Langflow to 1.13.0 if you run it, and check whether it's reachable from the internet at all. Second, audit every place in your agent stack where a model can turn text into executed code or tool calls, and ask what inspects that text beforehand. If the answer is "nothing," you have the same shape of problem Langflow just disclosed, whatever framework you use.
Treat prompts and tool results as untrusted input. Scrub them before the agent acts.
Sentinel is self-hostable (Enterprise) or available as SaaS, with a free Starter tier and no credit card required: sentinelaifirewall.com
Sources
AI-assisted draft or imaging, human-curated, reviewed and edited.
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