CVE-2026-22038: AutoGPT Information Disclosure Flaw

CVE-2026-22038 Overview

A sensitive information disclosure vulnerability exists in the AutoGPT platform's Stagehand integration that logs API keys and authentication secrets in plaintext. AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. Prior to autogpt-platform-beta-v0.6.46, the Stagehand integration blocks log API keys and authentication secrets in plaintext using logger.info() statements. This occurs in three separate block implementations (StagehandObserveBlock, StagehandActBlock, and StagehandExtractBlock) where the code explicitly calls api_key.get_secret_value() and logs the result.

Critical Impact

API keys and authentication credentials are exposed in plaintext logs, potentially allowing attackers with log access to steal credentials and gain unauthorized access to connected services and AI model providers.

Affected Products

• AutoGPT Platform versions prior to autogpt-platform-beta-v0.6.46
• StagehandObserveBlock component
• StagehandActBlock component
• StagehandExtractBlock component

Discovery Timeline

• 2026-02-04 - CVE CVE-2026-22038 published to NVD
• 2026-02-05 - Last updated in NVD database

Technical Details for CVE-2026-22038

Vulnerability Analysis

This vulnerability falls under CWE-532 (Insertion of Sensitive Information into Log File), a category of information disclosure vulnerabilities where sensitive data is inadvertently written to log files. The issue affects the Stagehand integration within the AutoGPT platform, specifically in the backend blocks responsible for AI agent operations.

The vulnerable code explicitly retrieves secret values from credential objects and passes them to logging functions at the INFO level. This means any system with access to application logs—including log aggregation services, monitoring tools, or compromised log storage—could capture these plaintext credentials. The impact is significant as API keys for AI model providers (such as OpenAI, Anthropic, or other LLM services) could be exfiltrated and used for unauthorized API access, potentially resulting in financial loss and service abuse.

Root Cause

The root cause is improper logging practices in the Stagehand block implementations. The developers used logger.info() to output debug information that included sensitive credential data by calling get_secret_value() on credential objects. This method is designed to retrieve the actual secret value from a protected wrapper, and logging this output directly violates secure coding principles for handling authentication secrets.

Attack Vector

An attacker with access to application logs could exploit this vulnerability through several vectors:

1. Log File Access: Gaining read access to log files stored on disk or in cloud storage
2. Log Aggregation Services: Accessing centralized logging platforms (e.g., Elasticsearch, Splunk, CloudWatch) where logs are collected
3. Container/Process Monitoring: Capturing stdout/stderr streams from the running application
4. Backup Access: Obtaining log data from system backups

The network attack vector with low privileges required indicates that authenticated users with limited log access could potentially harvest credentials belonging to other users or the platform itself.

         **kwargs,
     ) -> BlockOutput:
 
-        logger.info(f"OBSERVE: Stagehand credentials: {stagehand_credentials}")
-        logger.info(
-            f"OBSERVE: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
+        logger.debug(f"OBSERVE: Using model provider {model_credentials.provider}")
 
         with disable_signal_handling():
             stagehand = Stagehand(

Source: GitHub Commit Update

Detection Methods for CVE-2026-22038

Indicators of Compromise

• Log entries containing strings like OBSERVE: Stagehand credentials: followed by credential data
• Log entries containing Model credentials: with secret: values in plaintext
• Unusual API activity from AI model providers indicating credential reuse from unauthorized sources
• Multiple authentication attempts using the same API keys from different IP addresses

Detection Strategies

• Search application logs for patterns matching get_secret_value() output or credential-related log messages
• Monitor for log entries containing API key formats (typically alphanumeric strings of specific lengths)
• Implement log scanning rules to detect sensitive data patterns such as API keys and tokens
• Review access logs for the log storage systems to identify unauthorized access

Monitoring Recommendations

• Enable alerting on any log entries containing keywords like api_key, secret, credentials in plaintext
• Implement automated log sanitization to redact sensitive patterns before storage
• Monitor AI model provider dashboards for unexpected API usage spikes
• Set up anomaly detection for API key usage patterns across different geographic locations

How to Mitigate CVE-2026-22038

Immediate Actions Required

• Upgrade AutoGPT platform to version autogpt-platform-beta-v0.6.46 or later immediately
• Rotate all API keys and credentials that may have been logged prior to the upgrade
• Review and purge existing log files that may contain exposed credentials
• Audit log storage access to identify any potential credential theft

Patch Information

The vulnerability has been patched in autogpt-platform-beta-v0.6.46. The fix modifies the logging behavior in the Stagehand blocks (StagehandObserveBlock, StagehandActBlock, StagehandExtractBlock) to use logger.debug() instead of logger.info() and removes the explicit logging of secret values. The patch is available via the GitHub Commit and is documented in the GitHub Security Advisory GHSA-rc89-6g7g-v5v7.

Workarounds

• If immediate upgrade is not possible, disable the Stagehand integration blocks temporarily
• Configure logging level to WARNING or higher to suppress INFO and DEBUG messages
• Implement log filtering at the collection layer to redact sensitive patterns before storage
• Restrict access to log files and log aggregation systems to essential personnel only

# Configuration example - Set logging level to suppress sensitive output
export LOG_LEVEL=WARNING

# Or in Python configuration
# logging.getLogger().setLevel(logging.WARNING)

# Review and purge sensitive log entries
grep -r "get_secret_value\|api_key\|Model credentials" /var/log/autogpt/ | tee credentials_audit.txt