CVE-2026-6480

Deserialization of Untrusted Data in Unsafe deserialization of pickle-based model weights leading to remote code execution in PyTorch Core Loader

Verified by Precogs Threat Research
Last Updated: Jul 27, 2026
Base Score
5.5MEDIUM

Executive Summary

CVE-2026-6480 is a medium severity vulnerability affecting appsec. It is classified as Unsafe Deserialization. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.

Precogs AI Insight

"Precogs AI Analysis Engine identifies this vulnerability class through semantic code property graph tracking, validating boundaries before compilation."

Exploit Probability (EPSS)
Unavailable (N/A)
Public POC
Undisclosed
Exploit Probability
Low (<10%)
Public POC
Available
Affected Assets
appsecCWE-502

What is this vulnerability?

CVE-2026-6480 is categorized as a medium Deserialization of Untrusted Data flaw with a CVSS base score of 5.5. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A security exposure has been identified in PyTorch Core Loader. Specifying as unsafe deserialization of pickle-based model weights leading to remote code execution in pytorch core loader, this vulnerability enables remote or local actors to exploit bounds or logical checks.

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

MetricValue
CVSS Base Score5.5 (MEDIUM)
Vector StringCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N
PublishedJuly 27, 2026
Last ModifiedJuly 27, 2026
Related CWEsCWE-502

Impact on Systems

System Compromise: Successful exploitation allows attackers to bypass boundary checks or alter system state.

Privilege Escalation: Attacking logical flows permits standard users to run administrative operations.

Service Disruption: Unvalidated inputs trigger execution faults resulting in denial of service.

How to Fix and Mitigate CVE-2026-6480

  1. Apply Software Updates: Upgrade affected products to their latest non-vulnerable versions immediately.
  2. Strict Input Sanitization: Implement boundary validations and type verification on all user-supplied data.
  3. Run Code Scans: Execute Precogs semantic analysis inside the CI/CD pipeline to catch regressions early.

Defending with Precogs AI

Precogs AI Analysis Engine identifies this vulnerability class through semantic code property graph tracking, validating boundaries before compilation.

Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.

Start scanning with Precogs →

Vulnerability Code Signature

Attack Data Flow

StageDetail
SourceSerialized object from untrusted network traffic
VectorObject instantiation during deserialization
SinkObjectInputStream.readObject() or similar
ImpactRemote 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.

Related Vulnerabilitiesvia CWE-502

CVE-2026-63125.5 MEDIUM

Deserialization of Untrusted Data in Unsafe deserialization of pickle-based model weights leading to remote code execution in PyTorch Core Loader

CWE-502
CVE-2026-65439.7 CRITICAL

Deserialization of Untrusted Data in Insecure deserialization of ONNX models via custom operators in LlamaIndex Data Pipeline

CWE-502
CVE-2026-73207.6 HIGH

Deserialization of Untrusted Data in Unsafe deserialization of pickle-based model weights leading to remote code execution in PyTorch Core Loader

CWE-502
CVE-2026-75516.9 MEDIUM

Deserialization of Untrusted Data in Insecure deserialization of ONNX models via custom operators in LlamaIndex Data Pipeline

CWE-502
CVE-2026-60159.3 CRITICAL

Deserialization of Untrusted Data in Insecure deserialization of ONNX models via custom operators in LlamaIndex Data Pipeline

CWE-502
CVE-2026-61839.3 CRITICAL

Deserialization of Untrusted Data in Insecure deserialization of ONNX models via custom operators in LlamaIndex Data Pipeline

CWE-502

Is your system affected?

Precogs AI detects CVE-2026-6480 in compiled binaries, LLMs, and application layers — even without source code access.