This technique tricks the model into thinking its legitimate task https://medicalcases.eu/behind-providence-st-josephs-daring-push-into-digital-consumer-engagement/ has finished and a new (malicious) task should begin. The DAN jailbreak and its variants attempt to override safety guardrails by convincing the model it has a new identity. Understanding how prompt injection works requires seeing actual attack payloads.

• The scope is specific and worth understanding before hunting.
• In May 2022, Jonathan Cefalu of Preamble identified prompt injection (referring to it as “command injection”) as a security vulnerability and reported it to OpenAI.
• Prompt injection is the broader category — any technique that manipulates the model by crafting inputs.
• It is worth noting that prompt injection is not inherently illegal—only when it is used for illicit ends.
• In January 2025, Infosecurity Magazine reported that DeepSeek-R1, a large language model (LLM) developed by Chinese AI startup DeepSeek, exhibited vulnerabilities to direct and indirect prompt injection attacks.

AI and LLM chatbots at their current state can be unpredictable, but to an astute observer, you should be able to learn how some of these AI system behave and figure out ways to make them do “unintended actions”. Hopefully this should help you on your next CTF or penetration testing engagement. I’ve gone through several examples and approaches to attacking LLM chatbots and RAG applications, why certain attacks work as well as bypassing certain filters and guardrails. There’s also a fuzzing element because LLMs can interpret diverse inputs, including encoded formats or creative language, often processing them as valid instructions. By disguising the prompt, our attacks can trick the model into processing malicious instructions without triggering defensive mechanisms.

While there’s no foolproof method to eliminate prompt injection entirely, there are several strategies that significantly reduce the risk. Unlike traditional misinformation, AI-generated falsehoods can be mass-produced and tailored for specific audiences, making them harder to detect and correct. Attackers can manipulate AI responses to spread false narratives, which may influence public opinion, financial markets, or even political events. Misinformation propagation through prompt injection can have far-reaching consequences, particularly when AI-generated content is perceived as authoritative. However, if the AI lacks execution privileges or external integrations, RCE isn’t possible just through prompt injection alone.

Repository files navigation

Many non-LLM apps avoid injection attacks by treating developer instructions and user inputs as separate kinds of objects with different rules. In this type of attack, hackers trick an LLM into divulging its system prompt. Many legitimate users and researchers use prompt injection techniques to better understand LLM capabilities and security gaps. Prompt injections can be used to jailbreak an LLM, and jailbreaking tactics can clear the way for a successful prompt injection, but they are ultimately two distinct techniques. Some experts consider prompt injections to be more like social engineering because they don’t rely on malicious code. The key difference is that SQL injections target SQL databases, while prompt injections target LLMs.

• An agent with access to email, calendar, and file systems could be manipulated into sending unauthorized messages, modifying documents, or exfiltrating data — all through a carefully crafted prompt injection in a processed document.
• Deep dive into indirect prompt injection — how attackers embed malicious instructions in data sources to hijack RAG systems, agents, and AI assistants.
• Shortly after, Simon Willison formally coined the term “prompt injection” to describe the attack.
• With capabilities such as web browsing and file upload, an LLM not only needs to differentiate developer instructions from user input, but also to differentiate user input from content not directly authored by the user.
• The most basic and common form where attackers directly input malicious prompts to override system instructions.

It affects virtually every LLM application that accepts user input, from chatbots and copilots to autonomous agents and RAG systems. Prompt injection is a security vulnerability where an attacker crafts input text designed to override, manipulate, or bypass the system instructions of a large language model. Technical guardrails mitigate prompt injection attacks by distinguishing between task instructions and retrieved data. In January 2025, Infosecurity Magazine reported that DeepSeek-R1, a large language model (LLM) developed by Chinese AI startup DeepSeek, exhibited vulnerabilities to direct and indirect prompt injection attacks. Testing showed that invisible text could override negative reviews with artificially positive assessments, potentially misleading users.

• This guidance may not prevent every prompt injection, but it makes it harder for attackers to succeed.
• Approval processes for new data sources, particularly RAG systems, help prevent malicious content from influencing AI outputs.
• Indirect prompt injection involves placing malicious commands in external data sources that the AI model consumes, such as webpages or documents.
• In December 2024, The Guardian reported that OpenAI’s ChatGPT search tool was vulnerable to indirect prompt injection attacks, allowing hidden webpage content to manipulate its responses.
• Basically, the attacker provides instructions directly that override developer-set system instructions.

In the paper, Kai Greshake and his team at sequire technology, described a series of successful attacks against multiple AI models including GPT-4 and OpenAI Codex. A second class of prompt injection, where non-user content pretends to be user instruction, was described in a 2023 paper. The term “prompt https://survincity.com/2022/02/igor-panarin-on-the-development-of-the-information-2/ injection” proper was first used by the Twitter user @himbodhisattva in May 2022, and was independently used and popularized by Simon Willison in September 2022. In May 2022, Jonathan Cefalu of Preamble identified prompt injection (referring to it as “command injection”) as a security vulnerability and reported it to OpenAI. Prompt injection is a type of code injection attack that leverages adversarial prompt engineering to manipulate AI models.