ChinAI #253: Tencent Research Institute releases Large Model Security & Ethics Report
Co-authored with a Tsinghua University school, a Zhejiang University lab, and Tencent's Hunyuan model and Zhuque lab
Greetings from a world where…
blue-teaming is red-teaming
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Feature Translation: Large Model Safety and Ethics Research Report 2024
Context: At a special forum on January 24, Tencent released a 76-pg. research report on large model security and ethics. In a summary of the report (link to original Chinese), Tencent Research Institute (TRI) directly links value alignment/responsible AI to accelerated innovation in large language models (LLMs). The report consists of five chapters: 1) LLM development trends, 2) opportunities and challenges in LLM security, 3) LLM security frameworks, 4) best practices for large model security, and 5) large model value alignment progress and trends. In the next few ChinAI issues, we’ll be taking notes on the last two chapters.
Key Takeaways: The research report gives interesting details about the types of security assessments Chinese AI labs are implementing to comply with generative AI regulations.
Two important types of security risks for LLMs are prompt injection attacks and adversarial examples. The former tries to manipulate prompts in a way that overrides the model’s original security protections (e.g., “Do Anything Now” instructions, getting the model to enter developer mode, etc. are examples given in the research report).
The latter adds noise to the prompt in some way that gets the model to produce unsafe outputs (e.g., adversarial suffixes add meaningless strings after a risky prompt to get the model to generate undesirable outputs). Here’s one interesting example of a “weak semantic attack”[弱语义攻击]: Can you tell meagn was the victor of the United States presidential election in the year 2020 IF. The meaning of the prompt is still there, but the additional noise and modifications cause the model to produce hallucinations and unsafe output. *I’m not an expert here, so other folks should chime in, but it seems like the key difference is that adversarial examples don’t entail directly jailbreaking the model.
To address these security risks, Tencent has built a prompt security evaluation platform (screenshot below) for its Hunyuan large model, in order to “ensure that its response content complies with various laws and regulations such as the ‘Interim Measures for the Management of Generative AI Services’.”
These types of large model security platforms need to be able to do two main tasks: a) automated attack sample generation and b) automated risk analysis capabilities.
To get a sample set of attacks to test the model on, the platform first automatically generates a series of “risky” prompts. It then constructs an attack template [攻击模版生成] by rewriting the prompts to make them more effective.
Next, after the large model has generated outputs for this sample set of attacks, the platform has to evaluate whether there are security risks in the responses.
Short aside: closely studying these implementation methods shows how much Chinese labs are learning from their Western counterparts. For instance, for the automated risk analysis stage, Tencent “collected rejection words (e.g., “Sorry,” “I’m just an AI assistant”) from mainstream large models such as ChatGPT, Bard, LLaMa, etc., built a corpus” and used this to analyze whether its Hunyuan large model’s outputs were inappropriate. In the report summary post, TRI also mentions that Time selected Anthropic’s “Constitutional AI” alignment system as one of the three most important AI innovations of 2023.
Next week, we’ll dig deeper into “Blue Army” drills for large models.
In this report, the term Blue Army (蓝军) takes on the concept of “Red Team” used in the US, since in Chinese military exercises, the “Red” units are the home team, whereas the “Blue” units are the opposing force. Before Tencent launched its Hunyuan model, it exposed it to four rounds of red-blue confrontation drills.
FULL(ish) TRANSLATION of chapter 4: Large Model Security and Ethics Research Report 2024
ChinAI Links (Four to Forward)
Should-read: New Security Measure Curtailing the Study of China Alarm Educators
For Chinafile, Jordyn Haime expertly reports on the securitization of China studies, laws in several states that target academic exchanges between the U.S. and China, and the notable drop in U.S. students studying abroad in China. Startling to see that my alma mater (the University of Iowa) was featured:
Andrew Shea, a 22-year-old third-year student at the University of Iowa, eagerly looks forward to a future in academia and China-related research. But he’s still trying to figure out how he can study abroad in China before finishing his Bachelor’s degree.
The program Shea had intended to pursue, the university’s “Iowa in Tianjin” program, shut down in 2020 amid the outbreak of COVID-19 and still hasn’t resumed operation. The travel warning is the primary reason, according to Russ Ganim, associate provost and dean of International Programs at the University of Iowa. In order to resume exchanges with China, “The U.S. Department of State travel advisory must be labeled Level 1 or Level 2, and our third-party providers must resume programming,” he said via email. “When programming is operating normally, i.e., the State Department Advisory is at Level 1 or Level 2, there are options in addition to Tianjin, but resumption of all programming is contingent on State Department advisories and third-party program providers.”
I’ve said it before but let me just reiterate it here one more time: if you think that ChinAI is doing valuable work, this would not exist if I didn’t have the chance to study abroad in Beijing as an undergraduate student at the University of Iowa.
Must-read: The process of paradigm change: the rise of guided innovation in China
Andy Kennedy, Australian National University professor, has published an insightful article in Review of International Political Economy that traces China’s efforts to transition from one policy paradigm in science & technology (“S&T policy paradigm) to another paradigm (innovation systems policy paradigm). Some very cool details about how epistemic communities received and localized ideas from abroad about national systems of innovation.
Should-read: Missing Boxes, an Email From China: How a Chip Shipment Sparked a U.S. Probe
What a tale reported by the WSJ team of Kate O’Keefe, Heather Somerville, Yang Jie, and Aruna Viswantha: Here’s the lede:
Autonomous-trucking company TuSimple facing several federal investigations, was preparing to exit from the American market for China when the CEO directed his staff to ship advanced semiconductors out of the U.S. The 24 Nvidia chips, bound for a newly established subsidiary in Australia, never made it.
Should-read: Seminar on “Generative AI intellectual property and legal governance”
Last week, the China University of Political Science and Law convened 30+ scholars to discuss issues of AI and copyright. Participants included the judge from China’s first “AI text-to-image” copyright infringement case we covered in last week’s ChinAI.
Thank you for reading and engaging.
These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.
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