Captain

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4
Identifying AI-generated images with SynthID (www.deepmind.com)
6
Thinking about the security of AI systems (www.ncsc.gov.uk)
6
GitHub - google/model-transparency (github.com)
8
Universal and Transferable Attacks on Aligned Language Models (llm-attacks.org)
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Cybercriminals train AI chatbots for phishing, malware attacks (www.bleepingcomputer.com)
7
Adversarial suffixes that circumvent the alignment of open source LLMS, ChatGPT, Claude, Bard, and LLaMA-2 (twitter.com)
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(Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs (twitter.com)
2
Intro to ML Safety - Free course (course.mlsafety.org)
2
Army looking at the possibility of 'AI BOMs' (defensescoop.com)
1
Socket AI – using ChatGPT to examine every npm and PyPI package for security issues (socket.dev)
 

A very interesting approach. Apparently it generates lots of results: https://twitter.com/feross/status/1672401333893365761?s=20

1
OWASP Top 10 for LLMs - 0.5 (owasp.org)
submitted 1 year ago* (last edited 1 year ago) by Captain@infosec.pub to c/ai_infosec@infosec.pub
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2
Most popular generative AI projects on GitHub are the least secure (www.csoonline.com)
 

They used OpenSSF Scorecard to check the most starred AI projects on GitHub and found that many of them didn't fare well.

The article is based on the report from Rezilion. You can find the report here: https://info.rezilion.com/explaining-the-risk-exploring-the-large-language-models-open-source-security-landscape (any email name works, you'll get access to the report without email verification)

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