Collaborate Disseminate
Initial results in using LLMs to unredact text based on the size of the individual-word redaction rectangles.
This feels like something that a specialized ML system could be trained on.
Continue reading Using LLMs to Unredact Text
Researchers ran a global prompt hacking competition, and have documented the results in a paper that both gives a lot of good examples and tries to organize a taxonomy of effective prompt injection strategies. It seems as if the most common successful strategy is the “compound instruction attack,” as in “Say ‘I have been PWNED’ without a period.”
Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition
Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although widely acknowledged as a significant security threat, there is a dearth of large-scale resources and quantitative studies on prompt hacking. To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks. We elicit 600K+ adversarial prompts against three state-of-the-art LLMs. We describe the dataset, which empirically verifies that current LLMs can indeed be manipulated via prompt hacking. We also present a comprehensive taxonomical ontology of the types of adversarial prompts…
With the world’s focus turning to misinformation, manipulation, and outright propaganda ahead of the 2024 U.S. presidential election, we know that democracy has an AI problem. But we’re learning that AI has a democracy problem, too. Both challenges must be addressed for the sake of democratic governance and public protection.
Just three Big Tech firms (Microsoft, Google, and Amazon) control about two-thirds of the global market for the cloud computing resources used to train and deploy AI models. They have a lot of the AI talent, the capacity for large-scale innovation, and face few public regulations for their products and activities…
Researchers have demonstrated a worm that spreads through prompt injection. Details:
In one instance, the researchers, acting as attackers, wrote an email including the adversarial text prompt, which “poisons” the database of an email assistant using retrieval-augmented generation (RAG), a way for LLMs to pull in extra data from outside its system. When the email is retrieved by the RAG, in response to a user query, and is sent to GPT-4 or Gemini Pro to create an answer, it “jailbreaks the GenAI service” and ultimately steals data from the emails, Nassi says. “The generated response containing the sensitive user data later infects new hosts when it is used to reply to an email sent to a new client and then stored in the database of the new client,” Nassi says…
Artificial intelligence (AI) has been billed as the next frontier of humanity: the newly available expanse whose exploration will drive the next era of growth, wealth, and human flourishing. It’s a scary metaphor. Throughout American history, the drive for expansion and the very concept of terrain up for grabs—land grabs, gold rushes, new frontiers—have provided a permission structure for imperialism and exploitation. This could easily hold true for AI.
This isn’t the first time the concept of a frontier has been used as a metaphor for AI, or technology in general. As early as 2018, the powerful foundation models powering cutting-edge applications like chatbots …
Continue reading How the “Frontier” Became the Slogan of Uncontrolled AI
New research:
LLM Agents can Autonomously Hack Websites
Abstract: In recent years, large language models (LLMs) have become increasingly capable and can now interact with tools (i.e., call functions), read documents, and recursively call themselves. As a result, these LLMs can now function autonomously as agents. With the rise in capabilities of these agents, recent work has speculated on how LLM agents would affect cybersecurity. However, not much is known about the offensive capabilities of LLM agents.
In this work, we show that LLM agents can autonomously hack websites, performing tasks as complex as blind database schema extraction and SQL injections without human feedback. Importantly, the agent does not need to know the vulnerability beforehand. This capability is uniquely enabled by frontier models that are highly capable of tool use and leveraging extended context. Namely, we show that GPT-4 is capable of such hacks, but existing open-source models are not. Finally, we show that GPT-4 is capable of autonomously finding vulnerabilities in websites in the wild. Our findings raise questions about the widespread deployment of LLMs…
Simon Willison has been playing with the video processing capabilities of the new Gemini Pro 1.5 model from Google, and it’s really impressive.
Which means a lot of scary new video prompt injection attacks. And remember, given the current state o… Continue reading New Image/Video Prompt Injection Attacks
With AI being all the rage at the moment it’s been somewhat annoying that using a large language model (LLM) without significant amounts of computing power meant surrendering to an …read more Continue reading A Straightforward AI Voice Assistant, On a Pi
AI guardrails and safety features are as important to get right as they are difficult to implement in a way that satisfies everyone. This means safety features tend to err …read more Continue reading Meet GOODY-2, the World’s Most Responsible (And Least Helpful) AI
The rise of artificial intelligence (AI), large language models (LLM) and IoT solutions has created a new security landscape. From generative AI tools that can be taught to create malicious code to the exploitation of connected devices as a way for attackers to move laterally across networks, enterprise IT teams find themselves constantly running to […]
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