Author: gem

The Register has a great interview with Ilia Shumailov on the number one risk of LLMs. He calls it “model collapse” but we like the term “recursive pollution” better because we find it more descriptive. Have a look at the article.

Our work at BIML has been deeply influenced by Shumailov’s work. In fact, he currently has two articles in our Annotated Bibliography TOP 5.

Here is what we have to say about recursive pollution in our work — An Architectural Risk Analysis of Large Language Models:

1. [LLMtop10:1:recursive pollution] LLMs can sometimes be spectacularly wrong, and confidently so. If and when LLM output is pumped back into the training data ocean (by reference to being put on the Internet, for example), a future LLM may end up being trained on these very same polluted data. This is one kind of “feedback loop” problem we identified and discussed in 2020. See, in particular, [BIML78 raw:8:looping], [BIML78 input:4:looped input], and [BIML78 output:7:looped output]. Shumilov et al, subsequently wrote an excellent paper on this phenomenon. Also see Alemohammad. Recursive pollution is a serious threat to LLM integrity. ML systems should not eat their own output just as mammals should not consume brains of their own species. See [raw:1:recursive pollution] and [output:8:looped output].

Another excellent piece, this time in the politics, policy, business and international relations press is written by Peter Levin. See The real issue with artificial intelligence: The misalignment problem in The Hill. We like the idea of a “mix master of ideas” but we think it is more of a “mix master of auto-associative predictive text.” LLMs do not have “ideas.”

What’s the difference (philosophically) between Adversarial AI and Machine Learning Security? Once again, Rob Lemos cuts to the quick with his analysis of MLsec happenings. It helps that Rob has actual experience in ML/AI (unlike, say, most reporters on the planet). That helps Rob get things right.

We were proud to have our first coverage come from Rob in darkreading.

My favorite quote: “Those things that are in the black box are the risk decisions that are being made by Google and Open AI and Microsoft and Meta on your behalf without you even knowing what the risks are,” McGraw says. “We think that it would be very helpful to open up the black box and answer some questions.”

Read BIML’s An Architectural Risk Analysis of Large Language Models (January 24, 2024)

Have a listen to Google’s cloud security podcast EP150 Taming the AI Beast: Threat Modeling for Modern AI Systems with Gary McGraw, the episode is tight, fast, and filled with good information.

• Gary, you’ve been doing software security for many decades, so tell us: are we really behind on securing ML and AI systems? 
• If not SBOM for data or “DBOM”, then what? Can data supply chain tools or just better data governance practices help?
• How would you threat model a system with ML in it or a new ML system you are building? 
• What are the key differences and similarities between securing AI and securing a traditional, complex enterprise system?
• What are the key differences between securing the AI you built and AI you buy or subscribe to?
• Which security tools and frameworks will solve all of these problems for us? 

Dang. Darkreading went and published our world domination plan for machine learning securiy

“To properly secure machine learning, the enterprise needs to be able to do three things: find where machine learning is being used, threat model the risk based on what was found, and put in controls to manage those risks.

‘We need to find machine learning [and] do a threat model based on what you found,’ McGraw says. ‘You found some stuff, and now your threat model needs to be adjusted. Once you do your threat model and you’ve identified some risks and threats, you need to put in some controls right across all those problems.’

There is no one tool or platform that can handle all three things, but McGraw happens to be on the advisory boards for three companies corresponding to each of the areas. Legit Security finds everything, IriusRisk helps with threat modeling, and Calypso AI puts controls in place.

‘I can see all the parts moving,’ McGraw says. ‘All the pieces are coming together.'”

Ah the Trinity of MLsec explained! Read the article here.

Apparently there are many CISOs out there who believe that their enterprise policies prohibit the use of ML, LLMS, and AI in their organization. Little do they know what’s actually happening.

This excellent article by darkreading discusses the first thing a CISO should do to secure AI: Find AI. The system described here is implemented by Legit Security.

BIML provided a preview of our upcoming LLM Risk Analysis work (including the top ten LLM risks) at a Philosophy of Mind workshop in Rio de Janeiro January 5th. The workshop was organized by David Chalmers (NYU) and Laurie Paul (Yale).

A tongue-in-cheek posting about the meeting can be found here.