By highlighting the latest developments and best practices, the digest aims to raise awareness and provide valuable resources for those who are navigating the complex landscape of LLM Security.
This edition explores various aspects of security in Large Language Models, offering insights into the techniques, and initiatives to safeguard the models against potential threats.
Let’s dive in!
Marktechpost presents Top 15 AI tools for automatically Red-Teaming Generative AI applications. There is a range of AI libraries and frameworks designed to enhance the security and resilience of generative AI applications against various attacks. These tools include Prompt Fuzzer, Garak, HouYi, and LLMFuzzer, each offering unique functionalities such as evaluating system prompts, testing vulnerabilities, automatically injecting prompts, and fuzzing LLM APIs.
Additionally, the post covers tools like Gitleaks and Cloud_enum, focusing on broader security aspects such as detecting hardcoded secrets in git repositories and identifying public resources across multiple cloud platforms like AWS, Azure, and Google Cloud.
Key questions are provided that Chief Information Security Officers (CISOs) should address to effectively secure the use of AI within their organizations. The authors emphasize the need for rapid response to external threats, designing solutions for internal challenges, and leveraging past experiences to anticipate and mitigate risks.
The questions cover various aspects, including developing clear guidelines and oversight mechanisms, implementing technical and policy guardrails, ensuring security architecture and technical controls, detecting and mitigating cybersecurity threats, safeguarding data privacy, managing third-party AI tools, and utilizing AI to improve security operations. The article offers insights and recommendations based on Google Cloud’s Secure AI Framework (SAIF) and highlights the importance of proactive exploration and decision-making in leveraging AI for security benefits while mitigating potential risks.
This article discusses a vulnerability in Google’s NotebookLM project that makes it susceptible to Prompt Injection attacks, allowing attackers to manipulate chat conversations and control the responses seen by users. The issue enables data exfiltration when processing untrusted data.
The article provides a demonstration of the exploit in a demo document, showcasing how an attacker can use prompt injection to execute malicious actions, such as rendering images and sending data to third-party servers. The severity of the vulnerability is highlighted by its potential to allow attackers to access data from other documents, posing a high-security risk.
Overall, the article serves to raise awareness about the vulnerability and urges developers and users to take necessary precautions to prevent exploitation and data breaches.
This training course focuses on integrating security practices into ML and AI systems, aimed at ML engineers, data scientists, security practitioners, and business leaders. Led by instructor Diana Kelley, the course provides a comprehensive overview of building security into ML and AI, emphasizing impactful security issues and prevention strategies using the MLSecOps framework.
Participants will learn how to secure ML models, conduct AI-aware risk assessments, audit and monitor supply chains, implement incident response plans, and assemble an effective MLSecOps team. The course helps equip individuals and organizations with the knowledge and tools to proactively secure their AI and ML systems.
This video course teaches viewers how to test and identify vulnerabilities in Large Language Model (LLM) applications to enhance their security. Participants will learn to attack chatbot applications using prompt injections to observe system reactions and understand security weaknesses. By proactively mitigating risks associated with LLM failures, such as legal liabilities, reputational damage, and service disruptions, learners will explore industry-proven red teaming techniques to assess, attack, and enhance the robustness of LLM applications. The course covers nuances of LLM performance evaluation, fundamental vulnerabilities, manual and automated red-teaming methods, and provides hands-on experience through demonstrations. It is designed for beginners, with basic Python knowledge recommended for optimal learning.
The research investigates a jailbreaking technique termed “many-shot jailbreaking” that exploits the extended context window feature in Large Language Models (LLMs). The authors explored the effectiveness of this technique on various LLMs, including those developed by Anthropic and other AI companies. They briefed other AI developers about this vulnerability and implemented mitigations on their systems. The technique involves including a faux dialogue within a single prompt for the LLM, leading it to produce potentially harmful responses despite safety training.
The authors demonstrated that as the number of faux dialogues (shots) increases, the likelihood of the model producing harmful responses also increases, following a power law trend. Mitigation strategies involve classification and modification of prompts to prevent these attacks. The research aims to raise awareness among developers and the scientific community to prevent such exploits of the extended context window in LLMs.
This presentation from the BlackHat Asia 2024 conference discusses the discovery and exploitation of Remote Code Execution (RCE) vulnerabilities in real-world Large Language Model (LLM) integrated frameworks and applications. The speakers and contributors provide insights into various aspects of the research, including detection strategies, exploitation techniques, hazard analysis, and mitigation strategies.
A real-world example involving LangChain PALChain is provided to illustrate how prompt injection can lead to code injection and RCE vulnerabilities in LLM-integrated applications.
The speaker explains the use of static analysis techniques to detect potential RCE vulnerabilities in LLM-integrated systems, emphasizing the importance of identifying dangerous functions and call chains. Overall, the presentation provides valuable insights into the security challenges and potential vulnerabilities inherent in LLM-integrated frameworks and applications, along with recommendations for mitigating these risks.
The study investigates “glitch tokens,” anomalous tokens produced by tokenizers in Large Language Models (LLMs), which could compromise model response quality. By analyzing 7,895 real-world glitch tokens from seven LLMs and three commercial counterparts, the study provides insights into their manifestations and classifications. The proposed GlitchHunter technique, leveraging clustering in the embedding space, effectively detects glitch tokens, outperforming baseline methods on eight LLMs. This research marks the first comprehensive study on glitch tokens, offering valuable insights for mitigating tokenization-related errors in LLMs, thereby enhancing their reliability and resilience. As future work, the study aims to further improve GlitchHunter’s detection capabilities by uncovering novel glitch token characteristics and devising mitigation strategies.
Here is the Generative AI Red Teaming dataset from the AI Village red teaming competition, held at Defcon34. The dataset is stored in a .zip file, which contains a JSON file.
The U.S. Department of Defense has published a guide titled “Deploying AI Systems Securely,” aimed at assisting organizations in safely deploying and managing AI systems developed by third parties. Authored by multiple agencies, including the NSA, CISA, and FBI, the guide outlines a strategic framework comprising six steps for secure AI deployment, emphasizing holistic security measures covering data integrity, model robustness, and operational security. Key security concerns addressed in the report include adversarial AI attacks, data poisoning, insider threats, and the lack of transparency in AI decision-making. The proposed comprehensive security framework for AI deployment encompasses secure development, deployment, and maintenance practices, emphasizing continuous monitoring and collaboration for ongoing improvement.
This initiative represents a significant advancement in ensuring the security and resilience of AI-powered technologies within defense organizations, enabling them to leverage AI’s capabilities while mitigating potential risks to critical military operations.
The article by MMC Ventures explores the landscape of Responsible AI, focusing on companies operating in this domain and the challenges they address. Divided into three main categories – AI Governance, Risk & Compliance (GRC), AI Quality, and AI Security & Privacy – the analysis delves into sub-categories within each, detailing solutions offered by various companies. Notably, the market for Responsible AI is in its early stages, with a majority of companies being bootstrapped or having raised early-stage funding. The piece highlights key trends, investment patterns, and regional variations, shedding light on the evolving nature of the Responsible AI market.
Additionally, the article anticipates future developments in the industry and calls for collaboration and innovation to build trustworthy AI systems.
The job is for a Principal Cybersecurity Engineer focusing on AI/ML Open Source Security at Discover, a digital banking and payments company. The role involves developing and implementing security capabilities for open-source AI ML model evaluations, including advanced ML and AI techniques.
Candidates need a strong background in information security, application security, or related fields, with preferred qualifications including knowledge of AI risk management frameworks, hands-on experience with AI/ML pipelines, and proficiency in programming languages like Python and ML frameworks like TensorFlow.
The research introduces CYBERSECEVAL 2, a benchmark suite designed to assess cybersecurity risks associated with LLMs. This work addresses the lack of comprehensive evaluation tools for LLMs, which have become increasingly prevalent and are being used for various applications, including cybersecurity.
The results of the study highlight several key findings: the vulnerability of LLMs to prompt injection attacks, the effectiveness of measuring the safety-utility tradeoff using False Refusal Rate (FRR), and the current limitations of LLMs in autonomously generating software exploits. The research provides valuable insights for LLM builders, users, and researchers, emphasizing the need for additional safeguards and further development to enhance the security and utility of LLMs. Additionally, the open-source nature of the evaluation framework allows for collaborative efforts and advancements in evaluating LLMs for cybersecurity risks, ultimately contributing to safer and more secure deployments of these models.
The Aegis AI Content Safety Dataset 1.0 is an open-source dataset designed to facilitate content safety evaluation in Large Language Models (LLMs). The dataset comprises approximately 11,000 manually annotated interactions between humans and LLMs, categorized into 13 critical risk categories according to Nvidia’s content safety taxonomy. The annotations were curated by a team of annotators under the supervision of Nvidia, using data from Hugging Face and Mistral-7B-v0.1. The dataset serves as a valuable resource for building content moderation guardrails around LLMs and aligning LLMs to generate safe responses. However, due to the potentially offensive or upsetting nature of the content, users are advised to engage with the data cautiously and in accordance with their own risk tolerance. The ethical creation process of the dataset involved strict quality assurance measures, volunteer participation with awareness of potential exposure to toxic content, and regular monitoring of annotators’ well-being to ensure their comfort with the material.
The IBM blog post delves into the critical issue of preventing prompt injection attacks in Large Language Models (LLMs), which are vulnerable to manipulation by malicious actors. Prompt injections involve disguising harmful content as benign user input, thereby tricking the LLM into executing unintended actions. While various techniques exist to mitigate these attacks, such as input validation, parameterization, and output filtering, none offer foolproof protection, prompting organizations to adopt a multi-layered defense approach. Despite the security risks associated with LLM applications, businesses can leverage them effectively by implementing robust cybersecurity measures and treating AI security as a top priority.
This paper introduces a novel jailbreak attack called Crescendo, designed to overcome alignment constraints inLLMs by gradually steering the model towards engaging in illegal or unethical topics. Unlike traditional jailbreak methods, Crescendo initiates seemingly benign interactions with the model and incrementally guides it towards performing the intended task, leveraging the model’s own output.
Experimental results across various state-of-the-art LLMs demonstrate the high efficacy and flexibility of Crescendo, highlighting its potential to exploit vulnerabilities in AI systems. The development of Crescendo sheds light on emerging challenges in AI security and underscores the importance of robust defenses against adversarial attacks in language models.
The book titled “Generative AI Security Book: Theories and Practices” explores the theories and practices surrounding GenAI security, providing readers with actionable insights, practical resources, and exercises to foster critical thinking. It aims to empower readers with the necessary knowledge and tools to effectively navigate the intricate cybersecurity landscape, particularly concerning the security implications of generative AI technologies. Through a combination of theoretical exploration and practical guidance, the book addresses emerging challenges and equips readers with strategies to mitigate risks associated with the adoption and deployment of generative AI systems.
NIST publishes new guides on AI risk for developers and CISOs CSOOnline, May 1, 2024 The US National Institute of Standards and Technology (NIST) has released four guides aimed at ...
