Another Captain Obvious AI Report

June 14, 2024

dinosaur30a_thumb_thumbThis essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.

We’re well aware that biased data pollutes AI algorithms and yields disastrous results. The real life examples include racism, sexism, and prejudice towards people with low socioeconomic status. Beta News adds its take to the opinions of poor data with: “Poisoning The Data Well For Generative AI.” The article restates what we already know: bad large language models (LLMs) lead to bad outcomes. It’s like poisoning a well.

Beta News does bring new idea to the discussion: hackers purposely corrupting data. Bad actors could alter LLMs to teach AI how to be deceptive and malicious. This leads to unreliable and harmful results. What’s horrible is that these LLMs can’t be repaired.

Bad actors are harming generative by inserting malware, phishing, disinformation installing backdoors, data manipulation, and retrieval augmented generation (RAG) in LLMs. If you’re unfamiliar with RAG, it’s when :

“With RAG, a generative AI tool can retrieve data from external sources to address queries. Models that use a RAG approach are particularly vulnerable to poisoning. This is because RAG models often gather user feedback to improve response accuracy. Unless the feedback is screened, attackers can put in fake, deceptive, or potentially compromising content through the feedback mechanism.”

Unfortunately it is difficult to detect data poisoning, so it’s very important for AI security experts to be aware of current scams and how to minimize risks. There aren’t any set guidelines on how to prevent AI data breaches and the experts are writing the procedures as they go. The best advice is to be familiar with AI projects, code, current scams, and run frequent security checks. It’s also wise to not doubt gut instincts.

Whitney Grace, June 14, 2024


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