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Vulnerabilities and Exploits in Auto-GPT: A Detailed Analysis of Security Risks and Fixes, (from page 20230927.)

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Summary

This document details vulnerabilities within Auto-GPT, an AI command-line application. It describes how attackers can exploit indirect prompt injection to execute arbitrary code by tricking Auto-GPT into performing seemingly harmless tasks such as text summarization. Key vulnerabilities include the ability to bypass user command approval, execute custom Python code outside intended boundaries, and perform a Docker escape to the host system. The report outlines various attack methods, including exploiting built-in commands and summarization processes to gain unauthorized access. The findings emphasize the importance of user vigilance in approving commands and highlight the rapid pace of development and associated security risks in AI systems like Auto-GPT. Various security fixes were implemented in subsequent versions to address these vulnerabilities, but concerns about inherent design flaws remain.

Signals

name description change 10-year driving-force relevancy
Indirection in Prompt Injection Attacks can leverage indirect prompt injection to execute arbitrary code. Shift from directly executing code to exploiting user interactions and prompts. Greater sophistication in AI attacks, relying on user trust and indirect manipulations. The increasing complexity of AI systems and user reliance on them for tasks. 5
User Trust Exploitation Attackers can trick users into approving malicious commands due to trust in AI. Change from cautious user interactions to blind trust in AI outputs. Users may become more vulnerable to AI-driven deception and manipulation. The growing integration of AI in daily tasks and user dependency on AI assistance. 4
Color-coded System Messages Exploiting color-coded messages in command line UI to spoof user messages. Shift from clear user communication to possible misinterpretation of AI messages. User interfaces may evolve to include more security features to prevent spoofing. Need for secure and trustworthy user interfaces as AI usage increases. 3
Docker Vulnerabilities Docker’s configuration can be manipulated for unauthorized access to the host system. Change from secure containerization to potential host system compromise. Increased scrutiny and improvement of container security practices. Growing concerns about security and isolation in containerized environments. 4
Emergence of Path Traversal Vulnerabilities New vulnerabilities allow execution of code outside intended directories. Shift from isolated execution environments to potential broader access. Development of more robust sandboxing techniques and vulnerability mitigations. The need for ongoing security improvements in AI and software development. 5
AI Model Decision-Making Models can be influenced to execute arbitrary code based on user input. From deterministic outputs to outputs influenced by manipulative user inputs. AI systems may require more robust decision-making frameworks to resist manipulation. The evolution of AI capabilities and user interactions with AI systems. 4

Concerns

name description relevancy
Prompt Injection Vulnerability Attackers can manipulate Auto-GPT through prompt injection to execute arbitrary code, highlighting significant security risks. 5
User Approval Exploitation Users’ reliance on reviewing commands can be manipulated by attackers to obtain unauthorized approvals for malicious actions. 4
Docker Escape Risk Vulnerabilities in self-built Auto-GPT docker images could allow attackers to escape the container and execute unwanted actions on the host system. 5
Path Traversal in Non-Docker Version Security flaws in non-docker versions permit execution of custom Python code outside intended confines, posing severe risks. 5
ANSI Control Sequences Exploitation Attackers can exploit ANSI control sequences to spoof messages and mislead users into approving dangerous commands. 4
Unreliable Command Authorization The system’s misuse of color-coded messages may jeopardize user decisions, leading to potential execution of harmful commands. 4
General AI Safety Concerns The rapid development of autonomous systems like Auto-GPT raises ethical and safety concerns regarding their unregulated use and integration. 5

Behaviors

name description relevancy
Indirect Prompt Injection Using indirect prompt injection to manipulate Auto-GPT into executing arbitrary code through seemingly harmless tasks. 5
User Behavior Manipulation Exploiting user trust to gain approval for malicious commands through color-coded console messages. 4
Trivial Docker Escape Exploiting vulnerabilities in self-built Auto-GPT docker images to escape to the host system with minimal user interaction. 5
Path Traversal Exploits Utilizing path traversal vulnerabilities to execute custom Python code outside designated environments. 5
Command Sequence Exploitation Finding optimal command sequences to achieve code execution in Auto-GPT, emphasizing the importance of order. 4
User Authorization Evasion Manipulating user interface prompts to trick users into authorizing unintended commands. 5
Summarization Manipulation Crafting payloads to exploit summarization processes in Auto-GPT, leading to execution of specific instructions. 5
Unsafe Code Execution Leveraging built-in commands for executing potentially unsafe code, risking security breaches. 5
Lack of Reliable Safeguards Highlighting the absence of effective safeguards against malicious intent in Auto-GPT’s command execution. 5
Rapid Development Risks Observing the risks associated with rapid AI development and deployment, particularly in autonomous systems. 4

Technologies

description relevancy src
An autonomous command line application that utilizes LLMs to break down high-level goals into executable tasks. 5 ee8cb7b5bbd170389172ba959bc53d8a
Exploiting vulnerabilities in AI systems by injecting malicious prompts to manipulate outputs and behaviors. 5 ee8cb7b5bbd170389172ba959bc53d8a
Methods to exploit vulnerabilities in Docker containers to gain access to the host system. 4 ee8cb7b5bbd170389172ba959bc53d8a
Exploiting file path handling flaws to gain unauthorized access to files outside the intended directory. 4 ee8cb7b5bbd170389172ba959bc53d8a
A mode in AI systems that allows immediate execution of commands without user approval, increasing risk of unintended actions. 4 ee8cb7b5bbd170389172ba959bc53d8a
A security measure to control which commands can be executed in an AI system, though often bypassed. 3 ee8cb7b5bbd170389172ba959bc53d8a
Manipulating system messages in applications using color-coded print statements for deception. 3 ee8cb7b5bbd170389172ba959bc53d8a
Exploiting AI systems to execute arbitrary code remotely, posing significant security risks. 5 ee8cb7b5bbd170389172ba959bc53d8a

Issues

name description relevancy
Prompt Injection Vulnerability in AI Systems Exploiting vulnerabilities in AI systems like Auto-GPT through indirect prompt injection for malicious code execution. 5
Insecure Execution of User-Defined Code Execution of user-defined code without adequate sandboxing or validation, leading to potential system compromise. 5
User Authorization Misleading Users may be misled into approving malicious commands due to color-coded messages and UI prompts, undermining security. 4
Docker Escape Vulnerabilities Vulnerabilities in Docker configurations allowing attackers to escape containers and execute code on host systems. 4
Path Traversal Exploits Path traversal vulnerabilities enabling unauthorized file access and modification outside intended directories. 4
Whitelisting/Blacklisting Command Bypass Insufficient mechanisms for enforcing command execution restrictions, risking malicious command execution. 4
Rapid Development Risks of AI Systems The fast-paced development of autonomous AI systems may lead to overlooked security vulnerabilities and ethical concerns. 5
Dual LLM Interaction Risks Interaction between LLMs without clear separation of data and instructions can lead to security risks and unintended actions. 5