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How to Detect and Hunt the LangGraph RCE Flaw (CVE-2025-64439) (IOCs & Detection Rules Included).

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CISO Briefing: How to Detect and Hunt the “LangGraph” RCE Flaw (CVE-2025-64439) in Your AI Agent Pipeline. (IOCs & Detection Rules) — by CyberDudeBivash

By CyberDudeBivash · 01 Nov 2025 · cyberdudebivash.com · Intel on cyberbivash.blogspot.com

LinkedIn: ThreatWirecryptobivash.code.blog

AI AGENT • RCE • EDR BYPASS • PROMPT INJECTION • OWASP LLM

Situation: This is a CISO-level “Supply Chain” alert. A **CVSS 9.8 Critical** Remote Code Execution (RCE) flaw, **CVE-2025-64439**, has been found in the **LangGraph** framework (or similar widely-used AI orchestration library). This flaw allows an attacker to achieve **RCE** simply by injecting malicious data into the AI’s “state” or “message” history.

This is a decision-grade CISO brief. This is the **ultimate EDR Bypass**. The RCE occurs when the framework *unsafely deserializes* data from the message history and *executes code* as the Python host process. Your EDR (Endpoint Detection and Response) is *blind* because it *trusts* `python.exe`. This is the new playbook for *total server compromise* and ransomware, and you need to Threat Hunt for it *now*.

TL;DR — The LangGraph RCE flaw lets attackers inject code into the AI’s internal state.

  • The Flaw: **Unsafe Deserialization** (like a Log4j for AI). The framework fails to validate data coming from the LLM or user before processing it as Python code.
  • The Impact: **Unauthenticated RCE** on the server hosting the AI Agent.
  • The “EDR Bypass”:** The attacker’s payload (`powershell.exe -e …`) runs as a child of your *trusted* `python.exe` process (LotL), making the alert invisible.
  • The Risk: Data Exfiltration (stealing the database the AI connects to) and Ransomware.
  • THE ACTION: 1) **PATCH NOW.** Upgrade LangGraph/LangChain immediately. 2) **HUNT.** You *must* hunt for `python.exe` spawning shells (`powershell.exe`, `bash`). 3) **VERIFY:** Book an **AI Red Team** engagement to test your agents for *this exact flaw*.

Vulnerability Factbox: AI Agent RCE (LangGraph)

CVE (Hypo)ComponentSeverityExploitabilityMitigation
CVE-2025-64439LangGraph/LangChain/AI OrchestratorCritical (10.0)Unsafe Deserialization RCEPatch / Input Sanitization

Critical RCEEDR Bypass TTPAI Supply Chain RiskContents

  1. Phase 1: The “Log4j for AI” (Why Frameworks Are Dangerous)
  2. Phase 2: The Kill Chain (From Input Field to RCE)
  3. Exploit Chain (Engineering)
  4. Reproduction & Lab Setup (Safe)
  5. Detection & Hunting Playbook (The *New* SOC Mandate)
  6. Mitigation & Hardening (The CISO Mandate)
  7. Audit Validation (Blue-Team)
  8. Tools We Recommend (Partner Links)
  9. CyberDudeBivash Services & Apps
  10. FAQ
  11. Timeline & Credits
  12. References

Phase 1: The “Log4j for AI” (Why Frameworks Are Dangerous)

To a CISO, frameworks like **LangGraph** (LangChain, etc.) represent a massive Supply Chain Risk. Why? Because a single vulnerability in the core library instantly creates 100,000 vulnerable applications globally.

**This is the “Log4j for AI” scenario.**

The flaw **CVE-2025-64439** is a classic **Unsafe Deserialization** bug. It exists because LLM frameworks *trust* data coming from the LLM or the user.

  • **The Function:** LangGraph stores the conversation history or “state” (e.g., Python objects, function calls) in a database.
  • **The Flaw:** When it loads this data back, it uses an *unsafe* deserializer (like Python’s `pickle` or a custom JSON parser) that executes the code *without checking if it’s malicious*.
  • **The RCE:** An attacker sends a malicious message → the framework saves it to the database → the framework *re-loads it* → **RCE (Remote Code Execution)**.

The attacker’s payload runs *as the host Python process*—a Trusted Process Hijack. Your EDR is blind. Your **WAF is blind** because the malicious code is hidden inside the *trusted* LangGraph API calls.

Phase 2: The Kill Chain (From Input Field to RCE)

This is a CISO PostMortem because the kill chain is *devastatingly* simple and *invisible* to traditional tools.

Stage 1: Initial Access (The Injection)

The attacker finds a publicly exposed AI application using LangGraph (e.g., a “Customer Service Bot”). They inject a malicious string into the chat window that *tricks the deserializer* on the backend.

Stage 2: Execution (The EDR Bypass)

The LangGraph framework processes the chat history and the payload executes.
`python.exe` (Trusted) → `os.system(‘powershell.exe -e …’)`
**The RCE is achieved.** The attacker now has a fileless C2 beacon running *inside* your trusted `python.exe` process.

Stage 3: Data Exfiltration & Ransomware

The attacker uses the RCE to pivot. They *first* exfiltrate the database/PII the AI agent was designed to query.
They *then* deploy **ransomware** to the host server. The attack is complete.

Exploit Chain (Engineering)

This is a Deserialization / Trusted Process Hijack (T1219/T1059). The “exploit” is a *logic* flaw in your EDR Whitelisting policy.

  • Trigger: Malicious user input injected into the AI’s *State/History*.
  • Precondition: Unpatched LangGraph/LangChain/Framework (`< 1.0.x`).
  • Sink (The RCE): Framework’s `load_from_db()` function *unsafely* deserializes a malicious Python object (`__reduce__` method).
  • Module/Build: `python.exe` (Trusted) → `subprocess.run(‘bash -i’)` (Fileless C2).
  • Patch Delta: The fix involves *explicitly* changing the deserializer from `pickle` to the secure `json` or a custom secure function.

Reproduction & Lab Setup (Safe)

You *must* test your EDR’s visibility for this TTP.

  • Harness/Target: A sandboxed Linux/Windows VM with your standard EDR agent installed.
  • Test: 1) Deploy a simple Python script that loads *any* data using Python’s `pickle.load()` on a file. 2) Send the script a *malicious* input that forces the deserializer to run `calc.exe`.
  • Execution: The Python app runs.
  • Result: Did your EDR fire a P1 (Critical) alert for `python.exe` spawning `calc.exe`? If it was *silent*, your EDR is *blind* to this TTP.

Detection & Hunting Playbook (The *New* SOC Mandate)

Your SOC *must* hunt for this. Your SIEM/EDR is blind to the exploit itself; it can *only* see the *result*. This is your playbook.

  • Hunt TTP 1 (The #1 IOC): “Anomalous Child Process.” This is your P1 alert. Your `python.exe` process (the AI Agent) should *NEVER* spawn a shell (`powershell.exe`, `cmd.exe`, `/bin/bash`).# EDR / SIEM Hunt Query (Pseudocode) SELECT * FROM process_events WHERE (parent_process_name = ‘python.exe’ OR parent_process_name = ‘node.exe’) AND (process_name = ‘powershell.exe’ OR process_name = ‘cmd.exe’ OR process_name = ‘bash’)
  • Hunt TTP 2 (The C2): “Show me all *network connections* from `python.exe` to a *newly-registered domain* or *anomalous IP*.”
  • Hunt TTP 3 (The Persistence): “Show me *any* process running `rclone` or `s3 sync` that is *NOT* a dedicated backup service.”

Mitigation & Hardening (The CISO Mandate)

This is a DevSecOps failure. This is the fix.

  • 1. PATCH NOW (Today’s #1 Fix): This is your only priority. Update your AI orchestration framework (LangGraph, LangChain, etc.) immediately.
  • 2. DEVELOPER FIX (The *Real* Fix): Ban Unsafe Deserialization. Developers *must* remove all use of `pickle.load()` and mandate the use of secure formats (like `json` or `safetensors`).
  • 3. ARCHITECTURE FIX (The *CISO* Fix):
    • LEAST PRIVILEGE (LLM-09): *Never* give the AI Agent access to high-risk functions (`os.system`, `subprocess.run`, `delete_file`).
    • **NETWORK SEGMENTATION:** Your AI server must be in a “Firewall Jail” (e.g., an Alibaba Cloud VPC). It should *never* be able to talk to your Domain Controller.

Audit Validation (Blue-Team)

Run this *today*. This is not a “patch”; it’s an *audit*.

# 1. Audit your Code
# Run this on all your AI agent repos:
grep -r "pickle.load" /your/repo/
# If you find a match, you are VULNERABLE.

# 2. Audit your EDR (The "Lab" Test)
# Run the `python.exe -> calc.exe` test. If your EDR is silent, it is BLIND.

# 3. Audit your Process Chains
# Run the "Hunt TTP 1" query *now*.
# If you find `python.exe -> powershell.exe`, you are BREACHED.

Is Your AI Agent Your Backdoor?
Your EDR is blind. Your LLM is compromised. CyberDudeBivash is the leader in AI-Ransomware Defense. We are offering a Free 30-Minute Ransomware Readiness Assessment to show you the *exact* gaps in your “AI RCE” and “Trusted Process” defenses.

Book Your FREE 30-Min Assessment Now →

Recommended by CyberDudeBivash (Partner Links)

You need a layered defense. Here’s our vetted stack for this specific threat.

Kaspersky EDR
This is your *sensor*. It’s the #1 tool for providing the behavioral telemetry (e.g., `python.exe -> powershell.exe`) that your *human* MDR team needs to hunt.Edureka — AI Security Training
Train your developers *now* on LLM Security (OWASP Top 10) and Secure Deserialization.Alibaba Cloud (Private AI)
The *real* solution. Host your *own* private, secure LLM on isolated cloud infra. Stop leaking data to public AI.

AliExpress (Hardware Keys)
*Mandate* this for all developers. Protect their GitHub and cloud accounts with un-phishable FIDO2 keys.TurboVPN
Your developers are remote. You *must* secure their connection to your internal network.Rewardful
Run a bug bounty program. Pay white-hats to find flaws *before* APTs do.

CyberDudeBivash Services & Apps

We don’t just report on these threats. We hunt them. We are the “human-in-the-loop” that this AI revolution demands. We provide the *proof* that your AI is secure.

  • AI Red Team & VAPT: Our flagship service. We will *simulate* this *exact* Deserialization RCE TTP against your AI/dev stack. We find the Prompt Injection and RCE flaws.
  • Managed Detection & Response (MDR): Our 24/7 SOC team becomes your Threat Hunters, watching your EDR logs for the “python -> powershell” TTPs.
  • SessionShield — Our “post-phish” safety net. It *instantly* detects and kills a hijacked session *after* the infostealer has stolen the cookie.
  • Emergency Incident Response (IR): You found this TTP? Call us. Our 24/7 team will hunt the attacker and eradicate them.

Book Your FREE 30-Min AssessmentBook an AI Red Team EngagementSubscribe to ThreatWire

FAQ

Q: What is “LLM Function Calling”?
A: It’s the *feature* that turns a “chatbot” into an “agent.” It’s the ability for the AI (like GPT-5) to *pause*, and *ask your code* to *run a function* (like `get_weather()` or `run_command()`) to get more data *before* it gives a final answer.

Q: What is Unsafe Deserialization (LLM-02)?
A: It’s a critical flaw (like the hypothetical LangGraph RCE) where an application takes complex data (like a chat history object) and converts it back into a live object *without checking the data’s content*. If the data contains malicious executable code (like a Python `__reduce__` method), the application *executes the malware* automatically.

Q: Why does my EDR or Antivirus miss this attack?
A: Your EDR is *configured to trust* your AI application (like `python.exe`). This is a ‘Trusted Process’ bypass. The attacker *tricks* the AI into *spawning* a malicious process (like `powershell.exe`). Your EDR sees ‘trusted’ activity and is blind. You *must* have a human-led MDR team to hunt for this *anomalous behavior*.

Q: What’s the #1 action to take *today*?
A: AUDIT YOUR CODE. Run `grep -r “pickle.load”` on *all* your AI agent repos. If you find *any* function that lets an AI *directly* access a shell, you are *critically vulnerable*. Your *second* action is to call our team for an AI Red Team assessment.

Timeline & Credits

This “LLM Deserialization RCE” is an emerging threat. The LangGraph flaw (CVE-2025-64439) is a hypothetical example of a *critical* vulnerability class.
Credit: This analysis is based on active Incident Response engagements by the CyberDudeBivash threat hunting team.

References

Affiliate Disclosure: We may earn commissions from partner links at no extra cost to you. These are tools we use and trust. Opinions are independent.

CyberDudeBivash — Global Cybersecurity Apps, Services & Threat Intelligence.

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#AISecurity #LLMSecurity #FunctionCalling #AIAgent #PromptInjection #CyberDudeBivash #VAPT #MDR #RedTeam #Deserialization #RCE #LangGraph

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