CVE-2025-31491
[AutoGPT] Leakage of cross-domain cookies and protected headers in requests redirect
Executive Summary
CVE-2025-31491 is a high severity vulnerability affecting pii-secrets, ai-code, binary-analysis, appsec. It is classified as Information Exposure. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.
Precogs AI Insight
"This security defect is primarily driven by within Description ===========, allowing the insecure processing of malicious payloads. If successfully exploited, a malicious user could seize control of the underlying infrastructure and pivot to adjacent networks. Precogs identifies and blocks the exfiltration of personally identifiable information to alert security teams to imminent boundary violations."
What is this vulnerability?
CVE-2025-31491 is categorized as a critical Sensitive Data Exposure flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Description ===========
AutoGPT uses a wrapper around the requests python library, located in [autogpt_platform/backend/backend/util/request.py](http.
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 | April 11, 2025 |
| Last Modified | April 11, 2025 |
| Related CWEs | CWE-200 |
Impact on Systems
✅ Authentication Bypass: Leaked credentials allow attackers to impersonate legitimate users or systems.
✅ Data Breach: Exposed PII triggers regulatory violations (GDPR/CCPA) and massive reputational damage.
✅ Lateral Movement: Exposed API tokens can be used to pivot deeper into internal infrastructure.
How to fix this issue?
Implement the following strategic mitigations immediately to eliminate the attack surface.
1. Secret Management Migrate all hardcoded secrets to a secure vault (e.g., AWS Secrets Manager, HashiCorp Vault).
2. Data Masking Implement automated redaction for logs to prevent PII/credentials from leaking into observability platforms.
3. Automated Scanning Deploy Precogs Secrets Scanner in pre-commit hooks and CI pipelines to prevent secret commits.
Vulnerability Signature
// Generic Secrets Exposure Vector
// DANGEROUS: Hardcoded secrets in source control or logs
const apiKey = "sk_live_1234567890abcdef";
console.log(`Connecting to API with key $\{apiKey\}`);
// SECURED: Secrets fetched from environment at runtime
const apiKey = process.env.API_SECRET_KEY;
if (!apiKey) throw new Error("API configuration missing");
// Never log secrets
console.log('Connecting to API... [REDACTED]');
References and Sources
- NVD — CVE-2025-31491
- MITRE — CVE-2025-31491
- CWE-200 — MITRE CWE
- CWE-200 Details
- PII and Secrets Exposure
- AI Code Security Vulnerabilities
- Binary Analysis Vulnerabilities
- Application Security Vulnerabilities
Vulnerability Code Signature
Attack Data Flow
| Stage | Detail |
|---|---|
| Source | Application error or debug endpoint |
| Vector | Verbose error messages or sensitive metadata returned to the client |
| Sink | HTTP response |
| Impact | Information gathering, aids in further attacks |
Vulnerable Code Pattern
# ❌ VULNERABLE: Information Exposure
@app.errorhandler(500)
def internal_error(error):
# Taint sink: returns stack trace to user
return f"Internal Server Error: {error}", 500
Secure Code Pattern
# ✅ SECURE: Generic error message
@app.errorhandler(500)
def internal_error(error):
# Log the detailed error internally
app.logger.error(f"Server Error: {error}")
# Return a generic message to the user
return "An internal server error occurred.", 500
How Precogs Detects This
Precogs API Security Engine comprehensively audits all web endpoints to ensure verbose error messages and sensitive metadata are not exposed.\n