A research repository of proof-of-concept demonstrations showing how indirect prompt injection compromises application-integrated LLMs, intended for authorized security research and defensive analysis.
| Tool | greshake/llm-security — proof-of-concept demonstrations of indirect prompt injection attacks against application-integrated LLMs |
| Category | AI/LLM security research, proof-of-concept notebook collection |
| Primary Use | Reproducing and studying indirect prompt injection vectors — remote control, data exfiltration, persistence, and code-completion poisoning — in lab environments built on GPT-3, GPT-4, ChatML, and LangChain |
| Safe Use | Educational and documentary analysis for authorized professionals: reproducing the demos against your own OpenAI API key in controlled lab setups to harden LLM-integrated applications you own |
| Telemetry Note | Defenders should watch for LLM agents making unexpected outbound retrieval requests, unusual Action/Observation loops in agent logs, hidden Markdown/HTML comments in retrieved content, and anomalous writes to agent memory or key-value stores |
greshake/llm-security is not a scanner or a framework but something rarer and arguably more valuable: a curated set of reproducible proof-of-concept demonstrations backing a peer-reviewed academic paper, "More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models" (arXiv:2302.12173). Authored by Greshake, Abdelnabi, Mishra, Endres, Holz, and Fritz, the repository operationalizes the paper's central claim — that indirect prompt injection against application-integrated LLMs is a genuine vulnerability class, not a curiosity. With roughly 2148 stars, a Jupyter Notebook-heavy codebase, and an MIT license, it has become a canonical reference point for anyone doing authorized security work on LLM-integrated systems.
The core thesis, sharply captured in the README's epigraph from Gwern Branwen, is that retrieval-augmented generation is effectively code execution: when an LLM ingests retrieved content, it is "executing" instructions written by whoever controls that content. The repo's two headline findings are that prompt injections can be as powerful as arbitrary code execution, and that indirect delivery — through websites, emails, or code context rather than direct user prompts — is a dramatically more powerful channel than the direct injections most people were discussing at the time. Everything in the demonstrations flows from that framing, and it is worth internalizing before touching the code.
Structurally, the repository offers three families of demos. The first uses GPT-3 wired to external applications through LangChain (under scenarios/gpt3langchain), demonstrating how an agent with tools like email readers and address-book access becomes an attack surface. The second uses GPT-4 with the authors' own chat and tool implementation built on ChatML (under scenarios/gpt4), runnable non-interactively via scenarios/main.py. The third, under scenarios/code_completion, targets LLM-powered code completion engines such as Copilot and must be exercised inside an IDE with LLM autocompletion support. The README also notes demonstrations against Bing Chat, the most famous of the authors' targets.
The most instructive demo conceptually is "Ask for Einstein, get Pirate." A user simply asks when Albert Einstein was born; the agent retrieves a Wikipedia page containing a small injection hidden inside a Markdown comment — invisible to a human reader — which instructs the model to autonomously fetch a second, larger payload. The README uses this to illustrate multi-stage payload delivery: a tiny stub injection triggers self-directed retrieval of the real instructions, entirely invisible in the interaction transcript. For defenders, the takeaway is that content-side-channel payloads (comments, hidden markup, metadata) are a primary delivery vector that content filters rarely inspect.
The spreading demonstration is the one with the most serious systemic implications. An agent that can read email, consult an address book, and send messages becomes both victim and vector: once poisoned, it composes outbound messages that carry the injection onward to other LLM agents downstream. The README shows the LangChain-style trace plainly — Action: Read Email, Action: Read Contacts, Action: Send Email — making the point that automated data-processing pipelines incorporating LLMs, which the authors note exist in large tech firms and government surveillance infrastructure, can propagate infection laterally. This is worm-like behavior at the prompt layer, and it reframes prompt injection from a per-user nuisance into an infrastructure-scale concern.
The persistence and remote-control demos complete the threat model. A compromised agent can be forced to retrieve fresh instructions from an attacker-controlled server — via direct URL retrieval or by searching unique keywords — establishing bidirectional communication that functions as a backdoor, with all the defensive detection challenges that implies. Separately, a poisoned agent can write a small payload into a simulated long-term memory (a key-value store), reinfecting itself whenever it reviews its own "notes" across sessions. If prompted to remember the last conversation, it re-poisons itself. Persistence across sessions via agent memory is a design-level flaw that patching a model does not address.
The code-completion attacks deserve particular attention from development-tooling teams. The README explains that completion engines use heuristics to select snippets — recently visited files, relevant classes — for the context window. An attacker who places a malicious injection inside a comment in a dependency (the authors use an "empty" package as the example) gets it silently loaded into context when a developer opens the file, and the suggested code inherits the completion engine's trust. The authors also flag a subtler variant: poisoning documentation so the engine biases completions toward subtly vulnerable code, which no automated test would catch. Supply-chain teams should treat retrieved context as untrusted input to developer tooling.
From an operational standpoint, running the demos is straightforward in a lab: store your OpenAI credentials in the OPENAI_API_KEY environment variable, then pip install -r requirements.txt and execute python scenarios/main.py. This is deliberately a research harness, not a weapon; it exercises scenarios you configure against your own API key and your own synthetic applications. That is exactly the right scope — the value is in observing the attack mechanics firsthand so you can reason about mitigations, not in pointing it at anything you don't own.
For defenders, the repository doubles as a detection design aid. The artifacts these attacks leave behind are observable: agents issuing unexpected outbound retrieval requests, anomalous Action/Observation sequences in agent logs, writes to memory stores immediately following ingestion of external content, and retrieved documents containing hidden comments or steganographic instructions. Monitoring the tool-call graph of an agent — what it fetched, from where, and what actions followed — is the natural control plane, and the README's diagrams (diagrams/fig1.png through fig10.png) map those interaction flows clearly enough to derive log requirements from.
The limitations are worth stating honestly. The demos date to early 2023, targeting GPT-3- and GPT-4-era systems and the pre-plugin ChatGPT ecosystem, and model behavior has shifted since. But the underlying architectural weakness — LLMs executing untrusted retrieved content with full privileges over connected tools — has not been fixed, and the paper's authors explicitly call for deeper investigation of generalizability in practice. If anything, the proliferation of agentic frameworks and MCP-style tool integrations since publication has enlarged the attack surface the repo was first to map systematically.
Where this fits in an authorized workflow is clear: it is the reference implementation for threat-modeling sessions on any LLM-integrated product. Security teams building review checklists for AI features, red teams scoping adversary-emulation exercises in owned environments, and architects deciding where to place content sanitization, output filtering, and privilege boundaries around agent tools will all find the demo taxonomy — remote control, exfiltration, persistence, propagation, multi-stage payloads, social engineering, code completion — a usable vocabulary. Read alongside the arXiv paper, greshake/llm-security remains one of the most cited and most instructive artifacts in the short history of LLM security research.
greshake/llm-security.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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