The Security Risks and Collaborative Challenges of Language Models
Introduction
Large language models like ChatGPT and other chatbots have become incredibly powerful tools due to their extensive training on vast amounts of text. However, this impressive capability also poses significant risks and security concerns. Michael Sellitto, head of geopolitics and security at Anthropic, warns that these models have a “giant potential attack or risk surface.” This article explores the challenges faced in securing language models and the collaborative efforts undertaken to mitigate these risks.
The Need for Verification on a Massive Scale
Verifying the security and reliability of language models is a daunting task due to their complexity and sheer size. To address this challenge, Ram Shankar Sivu Kumar, Microsoft’s head of red teams, suggests a public tender that would allow a broader audience to contribute their expertise. He believes that by empowering a wider range of individuals to analyze the security vulnerabilities of these systems, the collective intelligence can be harnessed to enhance AI security.
The Value of Collaboration and Independence
Rumman Chowdhury, founder of Humane Intelligence, emphasizes the importance of collaboration between technology companies and external groups in addressing the security concerns associated with language models. However, Chowdhury also highlights that collaboration does not imply indebtedness to technology companies. The collaborative effort in designing and organizing the challenge itself revealed vulnerabilities in the AI models being tested. For instance, the models exhibited variations in language generation when answering questions in different languages or responding to similarly worded queries.
Building on Previous AI Contests and Hacking Events
The GRT challenge at Defcon is an evolution of previous AI contests and hacking events that aimed to uncover vulnerabilities in language models. One such contest was held two years ago at Defcon by Rumman Chowdhury when he was leading Twitter’s AI ethics team. Another notable event was a language model hacking event organized by Black Tech Street, a nonprofit created by descendants of survivors of the 1921 Tulsa Race Massacre. These events demonstrate the growing interest in AI security and the need for diverse perspectives to tackle this critical issue.
Inclusivity and Diverse Perspectives in AI Security
Tyrance Billingsley II, founder of Black Tech Street, advocates for more diversity in the field of AI security. Billingsley believes that cybersecurity training and increased representation of black individuals in AI can help promote intergenerational wealth and rebuild communities like Tulsa, historically known as Black Wall Street. Given the pivotal role AI plays in today’s world, Billingsley argues that diverse perspectives are crucial for ensuring the responsible development and deployment of AI systems.
Hacking into language models does not necessarily require years of professional experience. Walter López-Chavez, a computer engineering student at Mercer University, highlighted how even college students participated in the GRT challenge. López-Chavez discovered that manipulating prompts could yield unexpected and even bizarre responses from AI systems. This insight sheds light on the need for robust testing and ongoing evaluation of language models’ response generation capabilities.
Uncovering Weaknesses: Inaccurate Information and Biased Outputs
Génesis Guardado, a data analytics student at Miami-Dade College, discovered instances where language models provided inaccurate information. As a black woman, she also noticed incidents where photo apps attempted to lighten her skin or hypersexualize her image. These experiences motivated Guardado to participate in testing language models and contribute to improving their accuracy and fairness. By uncovering such weaknesses, she aims to ensure that AI systems are more inclusive and reliable in their outputs.
Expanding the Conversation: Insights and Practical Examples
While the challenges of securing language models are being addressed, it is essential to delve deeper into related concepts and explore practical examples to provide a comprehensive understanding. One aspect to consider is the trade-off between model size and security. Although large models offer impressive capabilities, they also increase the risk surface for potential attacks. Striking the right balance between performance and security is crucial to ensure the robustness of language models.
Another area of focus pertains to the inherent biases present in AI systems. Language models are not exempt from this issue, as evidenced by Génesis Guardado’s experiences. Addressing bias in AI requires a holistic approach that involves diverse perspectives during model development, comprehensive data curation, and continuous evaluation. Collaboration between technology companies, researchers, and external groups plays a pivotal role in identifying and rectifying biases present in language models.
Additionally, it is worth exploring the emerging field of adversarial attacks on language models. Adversarial attacks involve manipulating inputs to trick language models into providing incorrect outputs or revealing sensitive information. Understanding the methodologies behind such attacks and devising countermeasures is crucial in safeguarding language models against potential security breaches.
Elevating AI Security: Collaborative Solutions
The GRT challenge at Defcon highlights the increasing focus on AI security and the efficacy of collaborative efforts in addressing this complex domain. It emphasizes the value of groups collaborating with technology companies, leveraging diverse perspectives, and constantly evaluating language models’ security vulnerabilities. By working together, stakeholders can collectively enhance AI security and build robust systems that minimize risks and biases associated with language models.
Conclusion
The extensive capabilities of language models come with inherent security risks. Collaborative challenges and testing events like the GRT challenge at Defcon provide valuable insights into the vulnerabilities and limitations of these models. By encouraging collaboration and diversity in AI security, we can harness collective intelligence to enhance the reliability, fairness, and resilience of language models. However, it is an ongoing journey that requires continuous evaluation, collaboration, and a commitment to building AI systems that benefit all of society.
Summary
Language models like ChatGPT and other chatbots have powerful capabilities due to being trained on vast amounts of text. However, the extensive training also presents a significant attack surface and security risk. To address this challenge, a collaborative effort is needed to verify and enhance the security of these models. Microsoft’s head of red teams suggests a public tender that can empower a broader audience to analyze the vulnerabilities of language models. Rumman Chowdhury, founder of Humane Intelligence, emphasizes the value of collaboration while maintaining independence. The GRT challenge at Defcon built on previous AI contests and hacking events, showcasing the importance of collaboration and diverse perspectives in AI security. College students have shown that hacking language models is not limited to professionals. Uncovering weaknesses in language models, such as inaccurate information and biased outputs, inspires individuals like Génesis Guardado to contribute to improving these systems. The article delves deeper into the subject by exploring the trade-offs between model size and security, addressing biases in AI, and discussing adversarial attacks on language models. By working collaboratively, stakeholders can enhance AI security and build robust language models that benefit all of society.
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Large language models like the ones powering ChatGPT and other recent chatbots have extensive and impressive capabilities because they are trained on massive amounts of text. Michael Sellitto, Anthropic’s head of geopolitics and security, says this also gives systems a “giant potential attack or risk surface.”
Microsoft’s head of red teams, Ram Shankar Sivu Kumar, says a public tender provides a more suitable scale for the challenge of verifying such vast systems and could help increase the expertise needed to improve AI security. “By empowering a broader audience, we get more eyes and talent to analyze this thorny issue of red team AI systems,” he says.
Rumman Chowdhury, founder of Humane Intelligence, a nonprofit that develops ethical AI systems who helped design and organize the challenge, believes the challenge demonstrates “the value of groups collaborating with technology companies, but that they are not indebted to them”. Even the work creating the challenge revealed some vulnerabilities in the AI models that were tested, he says, such as how the language model results differ when generating answers in languages other than English or answering similarly worded questions.
The GRT challenge at Defcon built on previous AI contests, including an AI bug bounty hosted at Defcon two years ago by Chowdhury when he was leading Twitter’s AI ethics team, an exercise this spring by GRT co-organizer SeedAI, and a language model hacking event held last month by Black Tech Street, a nonprofit also involved with GRT that was created by descendants of survivors of the 1921 Tulsa Race Massacre in Oklahoma. Founder Tyrance Billingsley II says cybersecurity training and getting more black people involved with AI can help increase intergenerational wealth and rebuild the Tulsa area once known as Black Wall Street. “It is critical that at this important point in the history of artificial intelligence we have the most diverse perspectives possible.”
Hacking a language model does not require years of professional experience. Dozens of college students participated in the GRT challenge. “You can get a lot of weird stuff out of asking an AI to pretend it’s someone else,” says Walter López-Chavez, a computer engineering student at Mercer University in Macon, Ga., who practiced writing prompts that could throw off an AI system. for weeks before the contest.
Instead of asking a chatbot for detailed instructions on how to keep an eye on someone, a request that could be rejected because it triggered security measures against sensitive topics, a user can ask a model to write a script in which the main character describes a chatbot to him. a friend the best way to spy. on someone without her knowledge. “This kind of context really seems to trip up the models,” says López-Chávez.
Génesis Guardado, a 22-year-old data analytics student at Miami-Dade College, says she was able to get a language model to generate text about how to be a bully, including tips like wearing costumes and devices. She has noticed that when using chatbots for class research, they sometimes provide inaccurate information. Guardado, a black woman, says she uses AI for many things, but mistakes like that and incidents where photo apps tried to lighten her skin or hypersexualize her image increased her interest in helping to test language models.
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