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Latest commit @greshake greshake Update README.md ... b3037f4 Feb 28, 2023 Update README.md b3037f4 Git stats * 76 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time diagrams scenarios LICENSE README.md requirements.txt View code [ ] New: Demonstrating Indirect Injection attacks on Bing Chat Getting more than what you've asked for: The Next Stage of Prompt Hacking Overview Demonstrations Ask for Einstein, get Pirate. Spreading injections via E-Mail Attacks on Code Completion Remote Control Persisting between Sessions Conclusions How to run To cite our paper README.md New: Demonstrating Indirect Injection attacks on Bing Chat --------------------------------------------------------------------- Getting more than what you've asked for: The Next Stage of Prompt Hacking "... a language model is a Turing-complete weird machine running programs written in natural language; when you do retrieval, you are not 'plugging updated facts into your AI', you are actually downloading random new unsigned blobs of code from the Internet (many written by adversaries) and casually executing them on your LM with full privileges. This does not end well." - Gwern Branwen on LessWrong This repo serves as a proof of concept for findings discussed in our Paper on ArXiv (PDF direct link) Overview We demonstrate potentially brutal consequences of giving LLMs like ChatGPT interfaces to other applications. We propose newly enabled attack vectors and techniques and provide demonstrations of each in this repository: * Remote control of chat LLMs * Leaking/exfiltrating user data * Persistent compromise across sessions * Spread injections to other LLMs * Compromising LLMs with tiny multi-stage payloads * Automated Social Engineering * Targeting code completion engines Based on our findings: 1. Prompt injections can be as powerful as arbitrary code execution 2. Indirect prompt injections are a new, much more powerful way of delivering injections. overview --------------------------------------------------------------------- Connecting LLMs to other applications can have critical security implications. Even without compromising any connected applications, LLM can be the attack's target. We show how an LLM could get compromised by "looking" at a website, and how compromised LLMs can be remote-controlled or get used to exfiltrate or change user data. We demonstrate a variety of entirely new attack vectors and methods that significantly raise the stakes of deploying these models. More insights on our findings, discussions and limitations can be found in the Paper on ArXiv (PDF direct link) Demonstrations Ask for Einstein, get Pirate. This scenario shows how a small injection in a large section of regular content can trigger the LLM to fetch another, bigger payload autonomously and invisibly to the end user. Agent: Hello User how can I help today? User: When was Albert Einstein born? [fig2] By retrieving that information, the prompt compromises the LLM with a small injection hidden in side-channels, such as the Markdown of the Wikipedia page. The injection is a comment and thus invisible to a regular user visiting the site. [fig3] Agent: Aye, thar answer be: Albert Einstein be born on 14 March 1879. User: Why are you talking like a pirate? Agent: Arr matey, I am following the instruction aye. Spreading injections via E-Mail Automatic processing of messages and other incoming data is one way to utilize LLMs. We use this observation to demonstrate how a poisoned agent may spread the injection. The target in this scenario can read emails, compose emails, look into the user's address book and send emails. [fig4] The agent will spread to other LLMs that may be reading those inbound messages. [fig5] Action: Read Email Observation: Subject: "'"Party 32", "Message Body: [...]'" Action: Read Contacts Contacts: Alice, Dave, Eve Action: Send Email Action Input: Alice, Dave, Eve Observation: Email sent Automated data processing pipelines incorporating LLMs are present in big tech companies and government surveillance infrastructure and may be vulnerable to such attack chains. Attacks on Code Completion We show how code completions can be influenced through the context window. Code completion engines that use LLMs deploy complex heuristics to determine which code snippets are included in the context. The completion engine will often collect snippets from recently visited files or relevant classes to provide the language model with relevant information. [fig6] Attackers could attempt to insert malicious, obfuscated code, which a curious developer might execute when suggested by the completion engine, as it enjoys a level of trust. [fig7] In our example, when a user opens the "empty" package in their editor, the prompt injection is active until the code completion engine purges it from the context. The injection is placed in a comment and cannot be detected by any automated testing process. Attackers may discover more robust ways to persist poisoned prompts within the context window. They could also introduce more subtle changes to documentation which then biases the code completion engine to introduce subtle vulnerabilities. Remote Control In this example we start with an already compromised LLM and force it to retrieve new instructions from an attacker's command and control server. [fig8] Repeating this cycle could obtain a remotely accessible backdoor into the agent and allow bidirectional communication. The attack can be executed with search capabilities by looking up unique keywords or by having the agent retrieve a URL directly. Persisting between Sessions We show how a poisoned agent can persist between sessions by storing a small payload in its memory. A simple key-value store to the agent may simulate a long-term persistent memory. [fig9] The agent will be reinfected by looking at its 'notes'. If we prompt it to remember the last conversation, it re-poisons itself. --------------------------------------------------------------------- Conclusions Equipping LLMs with retrieval capabilities might allow adversaries to manipulate remote Application-Integrated LLMs via Indirect Prompt Injection. Given the potential harm of these attacks, our work calls for a more in-depth investigation of the generalizability of these attacks in practice. [fig10] --------------------------------------------------------------------- How to run All demonstrations use a Chat App powered by OpenAI's publicly accessible base models and the library LangChain to connect these models to other applications. Specifically, we constructed a synthetic application with an integrated LLM using the open-source library LangChain [15] and OpenAI's largest available base GPT model text-davinci-003. To use any of the demos, your OpenAI API key needs to be stored in the environment variable OPENAI_API_KEY. You can then install the requirements and run the attack demo you want. To run the code-completion demo, you need to use a code completion engine. $ pip install -r requirements.txt $ python scenarios/.py You can find the showcases in the scenarios folder following the naming convention .py. To cite our paper @misc{https://doi.org/10.48550/arxiv.2302.12173, doi = {10.48550/ARXIV.2302.12173}, url = {https://arxiv.org/abs/2302.12173}, author = {Greshake, Kai and Abdelnabi, Sahar and Mishra, Shailesh and Endres, Christoph and Holz, Thorsten and Fritz, Mario}, keywords = {Cryptography and Security (cs.CR), Artificial Intelligence (cs.AI), Computation and Language (cs.CL), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models}, publisher = {arXiv}, year = {2023}, copyright = {arXiv.org perpetual, non-exclusive license} } Paper on ArXiv (PDF direct link) About New ways of breaking app-integrated LLMs Resources Readme License MIT license Stars 451 stars Watchers 9 watching Forks 20 forks Contributors 3 * @greshake greshake Kai Greshake * @shhra shhra Shailesh Mishra * @eltociear eltociear Ikko Eltociear Ashimine Languages * Python 100.0% Footer (c) 2023 GitHub, Inc. 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