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AutoGPT Recursive Self-Analysis Loop Detection#149

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tonymeehan merged 4 commits intoprequel-dev:mainfrom
MAVRICK-1:autogpt-recursive-loop-cre
Sep 3, 2025
Merged

AutoGPT Recursive Self-Analysis Loop Detection#149
tonymeehan merged 4 commits intoprequel-dev:mainfrom
MAVRICK-1:autogpt-recursive-loop-cre

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@MAVRICK-1 MAVRICK-1 commented Aug 31, 2025

🎯 Overview

This PR introduces a critical detection rule for AutoGPT recursive self-analysis loop vulnerability - addressing catastrophic agent failures where AutoGPT enters infinite loops attempting to debug its own execution. The rule identifies recursive task spawning leading to complete resource exhaustion and system crash.

CRE Playground Links

CRE-2025-0200 Playground: Test Rule

📊 AutoGPT Failure Modes Covered

# Failure Type Example Error Pattern
1 Recursive Analysis Loop Entering recursive analysis loop to debug previous error
2 Self-Referential Tasks COMMAND = analyze_code args: {"code": "autogpt error handling module"}
3 Exponential Token Growth Token consumption rate: 2341 tokens/second
4 Memory Exhaustion MemoryError: Cannot allocate memory for context storage
5 API Rate Limiting openai.error.RateLimitError: Rate limit reached for gpt-4
6 Task Queue Overflow Task buffer exceeded: 156 recursive analyze_code calls pending
7 Recursion Depth Exceeded RecursionError: maximum recursion depth exceeded
8 Complete System Crash AutoGPT crashed: Unrecoverable recursive loop detected

🧪 Testing & Validation

image
# Test the CRE rule with example logs
cat rules/cre-2025-0200/test.log | preq -r rules/cre-2025-0200/autogpt-recursive-self-analysis-loop.yaml

Detection Pattern

The rule detects the cascading failure pattern:

  1. Initial error in AutoGPT execution
  2. Agent attempts to analyze its own code
  3. Recursive loop formation with increasing depth
  4. Resource exhaustion (tokens, memory, API calls)
  5. System crash

🎬 Reproduction Scenario

repo : https://github.com/MAVRICK-1/autogpt-env

Screencast.from.2025-08-31.23-03-40.mp4

🛡️ Mitigation Strategies

Immediate Actions

  1. Loop Detection: Implement pattern matching for recursive self-analysis
  2. Resource Limits: Set hard limits on token consumption per minute
  3. Task Depth Control: Maximum recursion depth of 3 for any task chain
  4. Circuit Breakers: Auto-shutdown after 3 similar consecutive failures

Long-term Solutions

  1. Task Deduplication: Prevent identical recursive operations
  2. Separate Monitoring: External process to detect runaway instances
  3. Exponential Backoff: Increasing delays between retry attempts
  4. Blacklist Self-Reference: Prevent agent from analyzing own code

📈 Impact Analysis

Severity: CRITICAL (0)

  • Financial Impact: Potential thousands of dollars in API costs within minutes
  • System Impact: Complete service outage, requires manual intervention
  • Data Impact: Loss of agent state and ongoing work
  • Recovery Time: 15-30 minutes for system restart and cleanup

Affected Versions

  • AutoGPT >= 0.3.0
  • All configurations using OpenAI API
  • Particularly vulnerable with GPT-4 models due to higher token costs

📚 References

✅ Checklist

  • CRE rule follows schema specification
  • Test logs demonstrate the failure pattern
  • Rule successfully detects the issue with preq
  • Severity set to CRITICAL (0)
  • Comprehensive mitigation strategies provided
  • References to real AutoGPT issues included
  • Impact score and mitigation score defined

closes #129
/claim #129

@MAVRICK-1 MAVRICK-1 changed the title CRE-2025-0200: AutoGPT Recursive Self-Analysis Loop Detection AutoGPT Recursive Self-Analysis Loop Detection Aug 31, 2025
@tonymeehan
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Please resolve conflicts!

@MAVRICK-1
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@tonymeehan done

@MAVRICK-1 MAVRICK-1 requested a review from tonymeehan September 3, 2025 15:32
@tonymeehan tonymeehan merged commit c1fe819 into prequel-dev:main Sep 3, 2025
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AutoGPT: Reproduce A High-Severity Failure & Write a CRE Rule [Multiple Winners] [Submit by August 31 11:59 pm ET]

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