AI-Exploits: Repo of multiple unauthenticated RCEs in AI tools

AI-Exploits: Repo of multiple unauthenticated RCEs in AI tools(github.com)

67 points by DanMcInerney 2 years ago | 18 comments

aftbit 2 years ago |

Is anyone using any of these services? The only one I actually recognize from their list[1] is Triton Inference Server.

1: https://github.com/protectai/ai-exploits/tree/main/nmap-nse

ianbutler 2 years ago | |

I recognize most of them, they're all pretty common orchestration, distributed computation, or experiment management tools. Maybe you're just not as integrated on the operations portion of the ML space?

wolftickets 2 years ago | |

[I work at Protect AI] - The goal here was initially relatively common tooling around MLOps/Data Science work. All ears here if you have some ideas for other projects to explore.

swatcoder 2 years ago | |

The purpose of the repo seems to be to collect an archive of what real-world vulnerabilities look like, to inform service implementors and security researchers in their future work.

I suppose I’m idly curious about the answer to your question too, but paying too much attention to the specific targets feels like it’s missing the point and purpose of the collection.

spmurrayzzz 2 years ago | |

h2o is definitely somewhat popular specifically for LLMs, but ray is certainly widely used for distributed training workloads

gumballindie 2 years ago |

No wonder people working in ai think ai will replace programmers, given the prevalent lack of experience with actual programming among them.

Having said that, the Achilles heel of ai is data. The lower the quality the more powerful the attack.

I imagine if someone wanted to mess about with it on a serious scale they’d go for the jugular - the data. Write content and create hundreds or thousands of code repositories with subtle issues and bang, you’ve compromised thousands and thousands of unsuspecting folks relying on ai to create code, or any other type of content.

dwringer 2 years ago | |

I'm not sure... hundreds or thousands of code repositories with subtle issues sounds like... the real world of code repositories. And I'd think through analogy and redundancy of some common algorithms, the LLM trained that way might conceivably be able to FIX many of those errors.

gumballindie 2 years ago | | |

Someone should build a poc. Ai doesnt know things other than what it’s ingested. So for such an attack to be successful you’d need to tilt the statistic towards problematic code. You’d need loads and loads of repositories but its definitely doable.

wolftickets 2 years ago | |

[I work at Protect AI] You're spot on for data being the jugular, interestingly with exploits like this as an attacker you could quickly go for attacking model content but also have credentials that would grant you access to data in many cases.

These tools can serve as the first opening but a sizable one when looking to attack an enterprise more broadly.

gumballindie 2 years ago | | |

Indeed. I am thinking that one way to protect data and ensure its integrity is to somehow use agents trained on trusted sources to validate that the content is secure? For instance to detect “injections” of malicious or ill written code. Same for other types of content, but difficult.

Suppose someone magically creates thousands of repositories that write about a specific way of doing c pointers but all allow for buffer overflows, or sql queries with subtle ways to inject strings.

One way to defend is each data source that goes into training is to have an ai agent asses the input sources.

But even so it’s extremely difficult to catch convoluted attacks (ie when an exploit can be made upon meeting certain criteria).

Until then i’d consider any code written by an ai and unsupervised by a competent person as potentially tainted.

swyx 2 years ago | | |

> Protect AI is the first company focused on the security of AI and ML Systems creating a new category we call MLSecOps.

alright i looked you up, congrats on your fundraising. is there like an OWASP top 10 vuln list for MLSecOps? does it differ between traditional ML apps and LLM apps?

waihtis 2 years ago |

Nice work, just saw these pop up on the official CVE feed

RomanPushkin 2 years ago |

How does it work? Can't understand from the description

byt3bl33d3r 2 years ago | |

(I work for ProtectAI) We added a quick demo to the Readme [1]

[1] https://github.com/protectai/ai-exploits?tab=readme-ov-file#...

friendlynokill 2 years ago | | |

> With the release of this repository, Protect AI hopes to demystify to the Information Security community what pratical attacks against AI/Machine Learning infrastructure look like in the real world and raise awareness to the amount of vulnerable components that currently exist in the AI/ML ecosystem. More vulnerabilities can be found here: November Vulnerability Report

pratical --> practical