CVE-2025-24357
[vllm] Malicious model to RCE by torch.load in hf_model_weights_iterator
Executive Summary
CVE-2025-24357 is a high severity vulnerability affecting appsec, ai-code. It is classified as Unsafe Deserialization. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.
Precogs AI Insight
"The fundamental weakness here is traced back to within ### Description, allowing the mishandling of memory allocation boundaries. If successfully exploited, a malicious user could seize control of the underlying infrastructure and pivot to adjacent networks. The Precogs AI's Code Property Graph analysis traces untrusted input to neutralize the threat at the source level."
What is this vulnerability?
CVE-2025-24357 is categorized as a critical Application Verification Flaw flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Description The vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggin.
This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.
Risk Assessment
| Metric | Value |
|---|---|
| CVSS Base Score | 8 (HIGH) |
| Vector String | N/A |
| Published | January 27, 2025 |
| Last Modified | January 27, 2025 |
| Related CWEs | CWE-502 |
Impact on Systems
✅ Unauthorized Access: Flaws in application logic can permit unauthorized interaction with protected APIs.
✅ Data Manipulation: Adversaries may alter critical application states, such as user roles or configurations.
✅ Service Disruption: Improper error handling or unvalidated inputs can lead to resource exhaustion.
How to fix this issue?
Implement the following strategic mitigations immediately to eliminate the attack surface.
1. Defense in Depth Implement multi-layered validation (client-side, API gateway, and server-side).
2. Least Privilege Ensure backend service accounts operate with the absolute minimum rights required.
3. Security Regression Testing Integrate automated semantic security scanning into the deployment pipeline.
Vulnerability Signature
// Generic Application Security Flaw (Node.js)
app.post('/api/update-profile', (req, res) =\> \{
// DANGEROUS: Mass Assignment / Object Injection
// Attacker can pass \{ "isAdmin": true, "email": "..." \}
User.update(\{ id: req.user.id \}, req.body);
// SECURED: Explicitly select permitted fields
const \{ email, displayName, bio \} = req.body;
User.update(\{ id: req.user.id \}, \{ email, displayName, bio \});
\});
References and Sources
- NVD — CVE-2025-24357
- MITRE — CVE-2025-24357
- CWE-502 — MITRE CWE
- CWE-502 Details
- Application Security Vulnerabilities
- AI Code Security Vulnerabilities
Vulnerability Code Signature
Attack Data Flow
| Stage | Detail |
|---|---|
| Source | Serialized object from untrusted network traffic |
| Vector | Object instantiation during deserialization |
| Sink | ObjectInputStream.readObject() or similar |
| Impact | Remote Code Execution (RCE) via gadget chains |
Vulnerable Code Pattern
// ❌ VULNERABLE: Unsafe deserialization
public Object deserialize(byte[] data) throws Exception {
ByteArrayInputStream bais = new ByteArrayInputStream(data);
ObjectInputStream ois = new ObjectInputStream(bais);
// Taint sink: instantiates arbitrary classes
return ois.readObject();
}
Secure Code Pattern
// ✅ SECURE: Type-restricted deserialization
public Object deserialize(byte[] data) throws Exception {
ByteArrayInputStream bais = new ByteArrayInputStream(data);
// Use ValidatingObjectInputStream (Apache Commons IO)
ValidatingObjectInputStream ois = new ValidatingObjectInputStream(bais);
ois.accept(SafeClass.class);
// Sanitized instantiation
return ois.readObject();
}
How Precogs Detects This
Precogs AI Analysis Engine natively intercepts unsafe deserialization sinks to prevent remote code execution via object instantiation.\n