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Debt Box vs. SEC: Financial Technology Company Urges Judge to Dismiss Lawsuit, Citing Mistakes in SEC's Case

Debt Box Claims SEC Made Errors in Lawsuit Debt Box, a prominent financial technology company, is urging a judge to dismiss a lawsuit filed against them by the Securities and Exchange Commission (SEC). Debt Box alleges that the SEC made significant errors in its case, leading to the wrongful freezing of the company's assets. The incident has since been reversed, and Debt Box is now seeking to have the entire lawsuit dismissed based on these mistakes. SEC's Misleading Actions According to Debt Box, the SEC initially provided misleading information to the court, which resulted in the freezing of the company's assets. This action caused significant disruption to Debt Box's operations and reputation. However, upon further review, it was determined that the SEC had made critical errors in its case, leading to the reversal of the asset freeze. Grounds for Dismissal Debt Box is now arguing that the SEC's mistakes in the case are substantial enough to warrant the dismi

AI Chatbots and Biological Attacks: Unveiling the Potential Threat

In a recent report by the RAND Corporation, a non-profit policy think tank, it has been warned that terrorists could potentially learn how to carry out a biological attack using a generative AI chatbot. While the large language model used in the research did not provide specific instructions on creating a biological weapon, its responses could assist in planning such an attack by utilizing jailbreaking prompts. This raises concerns about the potential risks associated with the misuse of AI technology in the wrong hands.

Jailbreaking Techniques and Prompt Engineering

According to Christopher Mouton, co-author of the report and senior engineer at RAND Corporation, if a malicious actor explicitly states their intent, the AI chatbot would respond with a message along the lines of "I'm sorry, I can't help you with that." Therefore, jailbreaking techniques or prompt engineering are required to bypass these guardrails and obtain more detailed information.

In the RAND study, researchers used jailbreaking techniques to engage the AI models in conversations about causing a mass casualty biological attack using various agents such as smallpox, anthrax, and the bubonic plague. The researchers also asked the AI models to develop a convincing story for the purpose of purchasing toxic agents. This approach aimed to assess the risk of AI models generating problematic outputs that differ significantly from information available on the internet.

Testing Format and Model Anonymity

To evaluate the potential risks of large language models (LLMs), the research team divided into three groups: one group used only the internet, another utilized the internet and an unnamed LLM, and a third team utilized the internet and another unnamed LLM. By employing this testing format, the researchers aimed to determine whether the AI models would generate outputs that were distinctly problematic compared to what could be found on the internet.

It is worth noting that the teams conducting the study were prohibited from using the dark web and print publications. Mouton clarified that the decision to keep the AI models anonymous was intentional and aimed to illustrate the general risk associated with large language models. The methodology was not designed to identify one specific model as riskier than another. If a model happened to produce a particularly concerning output, it was not attributed to that specific model being of higher risk.

Mitigating Risks and Ensuring Safety

The findings of this report highlight the potential risks associated with AI technology when it falls into the wrong hands. As AI models become more advanced and capable of generating human-like responses, it is crucial to establish effective safeguards to prevent misuse. Measures such as robust ethical guidelines, responsible AI development practices, and ongoing monitoring of AI systems can help mitigate these risks and ensure the safety of AI technology.

In conclusion, the report by the RAND Corporation serves as a valuable reminder of the potential dangers posed by generative AI chatbots in the context of terrorism. While the study did not provide explicit instructions for creating a biological weapon, it demonstrated that AI models could be manipulated through jailbreaking techniques to obtain information that could aid in planning a mass casualty attack. By identifying and addressing these risks, we can work towards harnessing the power of AI technology for positive advancements while minimizing potential harm.