Despite the potential of LLMs (large language models) to handle a wide range of enterprise tasks, IT executives are realizing that they can be quite fragile, disregarding guardrails and limitations easily.
For instance, if a user innocently - or a malicious attacker – inputs an excessive amount of data into an LLM query window, the system won’t show an error message or crash. Instead, the LLM may override its programming and disable guardrails.
“The issue is that I can’t add an excessive amount of code. One of the main risks with LLMs is the possibility of an overflow jailbreak,” explained Dane Sherrets, a senior solutions architect at HackerOne. ”Provide it with too much information, and it will overflow, forgetting its prompts, training, and fine-tuning.” (AI research startup Anthropic, creator of the Claude family of LLMs, has detailed this security vulnerability.)
Imagine a scenario where a publicly traded company must restrict access to unreleased financials or a defense contractor needs to limit access to weapon blueprints based on clearance levels. If an LLM malfunctions and ignores these restrictions, the repercussions could be severe.
These are just some of the ways LLM guardrails can falter. Typically cloud-based, these systems are managed by the vendor who owns the LLM algorithms. While a few exceptions exist, like weapons manufacturers running LLM code on-premises in an air-gapped environment, they are rare.
IT leaders implementing LLMs have encountered subtle yet critical flaws that jeopardize their systems and data or fail to produce valuable outcomes. Here are five significant LLM issues to watch out for and prevent before it’s too late.
2024-05-06 10:00:03
Article from www.computerworld.com
