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.
Once you learn that many of your new applications have ML built into them (often regardless of policy), what’s the next step? Threat modeling, of course. Irius Risk, the worldwide leader for threat modeling automation, announced a threat modeling library covering ML risks identified by BIML on October 26, 2023.
This is the first tool in the world to include ML risk as part of threat modeling automation. Now we’re getting somewhere.
Darkreading was the first publication to cover the news, and remains the best source for cutting edge stories about machine learning security. Read the original story here.
Decipher covers the White House AI Executive Order, with the last word to BIML. Read the article from October 31, 2023 here.
Much of what the executive order is trying to accomplish are things that the software and security communities have been working on for decades, with limited success.
“We already tried this in security and it didn’t work. It feels like we already learned this lesson. It’s too late. The only way to understand these systems is to understand the data from which they’re built. We’re behind the eight ball on this,” said Gary McGraw, CEO of the Berryville Institute of Machine Learning, who has been studying software security for more than 25 years and is now focused on AI and machine learning security.
“The big data sets are already being walled off and new systems can’t be trained on them. Google, Meta, Apple, those companies have them and they’re not sharing. The worst future is that we have data feudalism.”
Another challenge in the effort to build safer and less biased models is the quality of the data on which those systems are being trained. Inaccurate, biased, or incomplete data going in will lead to poor results coming out.
“We’re building this recursive data pollution problem and we don’t know how to address it. Anything trained on a huge pile of data is going to reflect the data that it ate,” McGraw said. “These models are going out and grabbing all of these bad inputs that in a lot of cases were outputs from the models themselves.”
“It’s good that people are thinking about this problem. I just wish the answer from the government wasn’t red teaming. You can’t test your way out of this problem.”
BIML was invited to Oslo to present its views on Machine Learning Security in two presentations at NBIM in October.
The first was delivered to 250+ technologists on staff (plus 25 or so invited guests from all around Norway). During the talk, BIML revealed its “Top Ten LLM Risks” data for the first time (pre-publication).
The second session was a fireside chat for 19 senior executives.
This positioning of the red teaming article is much better than the original AP story. Here is a pointer to the Fortune article from August 13, 2023.
The idea that machine learning security is exclusively about “hackers,” “attacks,” or some other kinds of “adversary,” is misguided. This is the same sort of philosophy that misled software security into a myopic overfocus on penetration testing way back in the mid ’90s. Not that pen testing and red teaming are useless, mind you, but there is way more to security engineering that penetrate and patch. It took us forever (well, a decade or more) to get past the pen test puppy love and start building real tools to find actual security bugs in code.
That’s why the focus on Red Teaming AI coming out of the White House this summer was so distressing. On the one hand…OK, the White House said AI and Security in the same sentence; but on the other hand, hackers gonna hack us outta this problem…not so much.
This red teaming nonsense is worse than just a philosophy problem, it’s a technical issue too. Just take a look at this ridiculous piece of work from Anthropic.
Red Teaming Language Models to Reduce Harms:
Methods, Scaling Behaviors, and Lessons Learned
Red teaming sounds high tech, mysterious and steeped in hacker mystique, but today’s ML systems won’t benefit much from post facto pen testing. We must build security into AI systems from the very beginning (by paying way more attention to the enormous swaths of data used to train them and the risks these data carry). We can’t security test our way out of this corner, especially when it comes to the current generation of LLMs.
It’s tempting to pretend we can sprinkle some magic security dust on these systems after they are built, patch them into submission, or bolt special security apparatus on the side. Unfortunately the world well knows what happens when we pretend to be hard at work on security yet what we’re actually doing is more akin to squeezing our eyes shut and claiming to be invisible. Just ask yourself one simple question, who benefits from a security circus in this case?
AP reporter Frank Bajak covered BIML’s angle in this worldwide story August 13, 2023.
Synopsys has a new podcast focused on building security in. Episode two features BIML’s own Gary McGraw discussing building security into Machine Learning.
Have a listen.
We are extremely pleased to announce that Katie McMahon has joined BIML as a permanent researcher.
Katie McMahon is a global entrepreneur and technology executive who has been at the leading edge of sound recognition and natural language understanding technologies for the past 20 years. As VP at Shazam, she brought the iconic music recognition app to market which went on to reach 2 billion installs and 70 billion queries (Acquired by Apple) and spent over a decade at SoundHound (NASDAQ:SOUN) bringing NLU technology and Voice AI products from lab to market. She has worked for Snap and most recently served as President & COO of Native Voice. She is Advisor to several early staged tech companies, including Neosensory, Valence Vibrations, NatureQuant, and Wia.io. McMahon is the lead inventor on several patents involving methods of Automatic Speech Recognition, Natural Language Understanding and Augmented Reality. She earned a BA in Political & Social Thought from The University of Virginia and has attended and completed coursework at Stanford, M.I.T. Sloan, the London School of Economics and Political Science, and most recently, earned the Corporate Board Readiness badge certificate from the Leavey School of Business at Santa Clara University in Silicon Valley. Katie is most interested in understanding how rapidly evolving AI and the wider tech landscape stands to impact business, society and humanity at large.
BIML just published a short popular press article on LLM risks. Have a look.
As the world is rapidly advancing technologically, it is vital to understand the implications and opportunities presented by Large Language Models (LLMs) in the realm of national security and beyond. This discussion will bring together leading experts from various disciplines to share insights on the risks, ethical considerations, and potential benefits of utilizing LLMs for intelligence, cybersecurity, and other applications.