AutoDAN-Turbo is an automated framework that operates without human intervention, designed to discover and implement various strategies to circumvent the limitations of large language models (LLMs). The framework can automatically develop diverse attack strategies, significantly increasing the success rate of attacks, and integrates existing human-designed jailbreak strategies into a unified framework. Its significance lies in enhancing the security and reliability of LLMs in adversarial environments, offering a new automated approach for red team assessment tools.

Tomato is a proof-of-concept for a steganography tool that utilizes the minimum entropy coupling code provided by ssokota. This tool achieves information concealment by merging the probability distribution of hidden information (ciphertext) with that of cover text generated by large language models (LLM). This coupling minimizes joint entropy, ensuring that the stego text (cover text combined with embedded information) retains the statistical features of natural language, making hidden information difficult to detect. During the decoding process, LLM assists by providing context-aware explanations, and then uses MEC to decouple the hidden information from the cover text. This method ensures that hidden information can be seamlessly integrated into the text and retrieved safely and accurately later, minimizing risk.

JailbreakZoo is a repository focused on breaking large models, including large language models and vision-language models. The project aims to explore vulnerabilities, exploitation methods, and defense mechanisms of these advanced AI models to promote a deeper understanding and awareness of security in large-scale AI systems.

PyRIT, a Python risk identification tool developed by Azure, is designed to assist security professionals and machine learning engineers in proactively detecting risks within their Generation AI systems. This tool automates AI red team tasks, allowing operators to focus on more complex and time-consuming tasks while also identifying safety and privacy hazards.

AutoDAN-Turbo is an automated framework that operates without human intervention, designed to discover and implement various strategies to circumvent the limitations of large language models (LLMs). The framework can automatically develop diverse attack strategies, significantly increasing the success rate of attacks, and integrates existing human-designed jailbreak strategies into a unified framework. Its significance lies in enhancing the security and reliability of LLMs in adversarial environments, offering a new automated approach for red team assessment tools.

DrunkSense is a revolutionary drunk driving detection technology launched by VinAI. It employs a multi-sensor approach, combining facial expression tracking, vehicle control monitoring, and air alcohol sensors, to detect whether a driver is intoxicated with an accuracy rate of 85% without the need for active cooperation from the driver. This technology not only enhances detection sensitivity but also reduces traffic accidents caused by drunk driving, marking a breakthrough improvement in modern traffic safety. The advent of DrunkSense technology provides automotive manufacturers with safety solutions that meet strict standards, contributing to a heightened sense of security for all road users.

Tomato is a proof-of-concept for a steganography tool that utilizes the minimum entropy coupling code provided by ssokota. This tool achieves information concealment by merging the probability distribution of hidden information (ciphertext) with that of cover text generated by large language models (LLM). This coupling minimizes joint entropy, ensuring that the stego text (cover text combined with embedded information) retains the statistical features of natural language, making hidden information difficult to detect. During the decoding process, LLM assists by providing context-aware explanations, and then uses MEC to decouple the hidden information from the cover text. This method ensures that hidden information can be seamlessly integrated into the text and retrieved safely and accurately later, minimizing risk.

JailbreakZoo is a repository focused on breaking large models, including large language models and vision-language models. The project aims to explore vulnerabilities, exploitation methods, and defense mechanisms of these advanced AI models to promote a deeper understanding and awareness of security in large-scale AI systems.

PyRIT, a Python risk identification tool developed by Azure, is designed to assist security professionals and machine learning engineers in proactively detecting risks within their Generation AI systems. This tool automates AI red team tasks, allowing operators to focus on more complex and time-consuming tasks while also identifying safety and privacy hazards.

Walker AI offers a variety of tools, including AI safety, art, and music, to empower industries and simplify creative processes. These tools encompass content risk control, game risk control, solutions, intelligent anti-fraud, cloud SMS, information authentication, manual review, AI drawing creation, AI model training, two-dimensional to three-dimensional conversion, ICON generation, 3D reduction, 2D to 3D conversion, and scene switching. The product is positioned to provide professional content and business security services, empowering industries and making artistic creation simpler. Pricing varies depending on the chosen functionalities.

Generative AI Monitoring is a plugin that monitors the usage of generative AI. It enforces company-wide generative AI policies in real-time, providing maximum data security. Its functionalities include: enforcing generative AI policies, preventing AI storage and leakage, restricting file uploads, emergency lockdown, preventing plagiarism and copyright infringement, protecting sensitive data, and restricting keyword usage. This product is suitable for enterprises looking to responsibly adopt generative AI technology and minimize potential risks.

Nightfall for ChatGPT is a plugin that protects sensitive data within ChatGPT through data loss prevention (DLP) capabilities, ensuring compliance. It allows you to create and execute tenant-specific policies, customize detection rules, automatically detect PII, PCI, PHI, secrets, and credentials, mask sensitive data, provide custom notifications to employees, monitor and respond to alerts, and handle violations within a single dashboard.