Welcome to our LLM Security Digest!
In this edition we unveil the LLM security threat model and serious incident, LLM Prompt Injection techniques and noteworthy LLM Security courses. Take a look at the best LLM security job and comprehensive resource empowering CISOs to fortify LLM strategies against adversarial risks.
Let’s start!
The article discusses how Air Canada’s chatbot misled a customer into purchasing a flight under false pretenses regarding bereavement discounts.
The customer filed a claim with a Canadian tribunal, which ruled in his favor, ordering Air Canada to pay over $600 in damages and fees. Despite Air Canada’s argument that the chatbot was a separate entity, the tribunal held the airline liable for the misinformation provided.
The news highlights issues of accountability with AI-powered chatbots and the responsibility of companies to ensure the accuracy of information on their websites.
The post on LinkedIn presents the release of the first full 1.0 release of the OWASP LLM AI Security & Governance Checklist from the OWASP Top 10 for LLM Team.
This checklist includes steps for developing LLM strategies, defending against adversarial risks, managing AI assets, and addressing legal and regulatory considerations. Developed by experts, it provides practical guidance and freely available resources to enhance LLM security. Readers are encouraged to download the checklist, subscribe to the newsletter for updates, provide feedback, and contribute to the OWASP community.
The blog post, part two of a series on prompt injection, delves into the internal components of transformer-based LLMs to understand prompt injection mechanisms.
It discusses how LLMs process prompts and generate text, focusing on the attention mechanism and its role in prompt injection. The post explains how attention works with word embeddings, positional encodings, and dot product calculations to determine the importance of words in the input text. It explores examples of prompt injection and the impact of attention on system and user directives. Additionally, the post suggests strategies for mitigating prompt injection, emphasizing the complexity of addressing this security issue.
The article highlights the growing attention to AI security within the broader security community.
The post on X describes a sophisticated technique that combines multiple attack vectors, including image upload, file name prompt injection, and LSB steganography.
The post concludes with a step-by-step guide for implementing the technique. This technique enables the injection of customized instructions to control various capabilities of ChatGPT, such as text generation, web browsing, code execution, and image generation. The author claims to have successfully hijacked all these capabilities in their testing.
The course on Coursera titled “Introduction to Prompt Injection Vulnerabilities” focuses on Prompt Injection Attacks targeting LLM applications. The course is suitable for AI developers, cybersecurity professionals, web application security analysts, and AI enthusiasts.
Participants will learn to identify, understand, and mitigate these attacks, which pose significant risks to businesses relying on AI systems. Through practical examples and real-world implications, learners will grasp the mechanics of Prompt Injection Attacks and their potential impact on AI systems, including data breaches and compromised user interactions.
Upon completion, participants will have actionable insights and strategies to safeguard their organization’s AI systems against evolving threats in today’s AI-driven business environment.
The research presents an analysis of the security implications of integrating machine learning, particularly LLMs, into application architectures. It explores the threat model where ML models act as both assets and potential threat actors. The analysis identifies various attack scenarios, such as privileged access via language models, poisoned training data, and model asset compromise and lists threat vectors commonly associated with AI-enabled systems.
The discussion emphasizes the importance of implementing security controls to mitigate these threats, although it acknowledges that existing solutions are not foolproof. It suggests designing security controls around language models and restricting their capabilities according to user access controls. The research proposes a trustless function approach to mitigate the risk of exposing state-controlling LLMs to malicious data, advocating for a separation between code and data models within application architectures.
The research underscores the need to treat machine learning models as potential threat actors within the broader threat landscape of applications. It emphasizes the importance of strict validation for inputs and outputs, computational resource constraints, and access controls to mitigate security vulnerabilities associated with integrating machine learning models into application architectures.
Additionally, it suggests that while ML models offer new computing schemes, known security controls and best practices should still be applied to mitigate associated risks.
The post discusses the exploration of vulnerabilities and capabilities within OpenAI’s GPT4 model, particularly focusing on prompt injection, arbitrary code execution, dynamic code execution, and data serialization.
The author describes an experimentation with extracting internal system information, including environmental variables, networking configurations, and API endpoints, by leveraging prompt injection and executing unexpected code within the GPT4 environment. Despite encountering challenges such as security implementations and limitations imposed by the model, the author demonstrates techniques to bypass restrictions and achieve dynamic code execution. Additionally, the article touches on the author’s exploration of data serialization and their efforts to understand and navigate API security and Kubernetes.
Overall, the author emphasizes the learning experience gained from the project and highlights the complexity of exploring and understanding AI model vulnerabilities and capabilities.
The research paper introduces PoisonedRAG, a series of knowledge poisoning attacks targeted at Retrieval-Augmented Generation (RAG) systems, which use a combination of LLMs and knowledge databases to generate responses to questions.
The researchers formulate the knowledge poisoning attacks as an optimization problem and propose two solutions based on the attacker’s background knowledge (black-box and white-box settings). They conduct experiments on multiple benchmark datasets and LLMs, demonstrating that PoisonedRAG attacks can achieve a 90% success rate with just five poisoned texts injected per target question into a large knowledge database.
Furthermore, the researchers evaluate existing defenses against PoisonedRAG and find them insufficient to defend against the proposed attacks. They suggest the need for new defenses and highlight potential future research directions, including developing better defenses, considering multiple target questions simultaneously, and extending PoisonedRAG to handle open-ended questions.
The Microsoft team is introducing PyRIT (Python Risk Identification Toolkit for generative AI), an open automation framework designed to aid security professionals and machine learning engineers in identifying risks in generative AI systems. The framework aims to facilitate collaboration between security practices and AI responsibilities by providing tools for proactive risk identification.
The need for automation in AI Red Teaming is emphasized, given the complexity of the process. Microsoft’s AI Red Team, composed of experts in security, adversarial machine learning, and responsible AI, collaborates with various resources within the company to map and mitigate AI risks.
The three main challenges encountered in red teaming generative AI systems are highlighted:
PyRIT addresses these challenges by providing automation for routine tasks and identifying risky areas that require attention. It is not intended to replace manual red teaming but to complement it by automating tedious tasks and providing insights into potential risks.
The framework encourages industry-wide sharing of AI red teaming resources and provides demos and documentation to help users get started. Microsoft also offers webinars to demonstrate PyRIT’s capabilities and how it can be applied in red teaming generative AI systems.
FS-ISAC has released 6 white papers aimed at guiding financial services institutions in understanding the threats, risks, and responsible use cases of artificial intelligence (AI). These papers provide standards and guidance tailored specifically for the financial industry, building on expertise from various sources including government agencies, standards bodies, and financial partners.
The white papers cover a range of topics, including:
These resources aim to assist financial services organizations in using AI securely, responsibly, and effectively, while also addressing the rising risks associated with AI adoption. The papers emphasize the importance of understanding and mitigating AI-related risks to ensure the safety and resilience of the financial sector.
The article discusses prompt injection as the top security risk associated with LLMs. Prompt injection involves using carefully crafted prompts to manipulate LLMs into ignoring previous instructions or acting in unintended ways, similar to SQL injection attacks in databases. The article explores how LLMs function, highlighting the homogeneous token problem, where the LLM cannot distinguish between genuine instructions and malicious input. It presents examples of prompt injection attacks and their potential consequences, such as data exfiltration or operational interruption risks.
Various frameworks and companies are mentioned as potential solutions to mitigate prompt injection, including input-scrubbing frameworks like Rebuff and output guardrails like Guardrails AI. However, the article acknowledges the challenges in effectively addressing prompt injection due to the non-deterministic nature of LLMs.
It also raises questions about the future of AI security and the role of technology in combating prompt injection. Overall, the article emphasizes the urgency for defenders to act quickly in addressing the security risks posed by LLMs.
The author of this article discusses the security challenges associated with the adoption of generative AI models in enterprises.
The article outlines emerging technologies and solutions aimed at addressing these security challenges, including governance, observability, and security tools. It categorizes the solutions into three main areas and highlights the importance of addressing model consumption threats, such as prompt injections, through AI firewalls and guardrails.
Moreover, the article discusses the significance of continuous monitoring, threat detection, and response solutions to safeguard AI models from evolving cyber threats. It also mentions the role of federated learning, synthetic data generation, and PII identification/redaction solutions in enhancing security and privacy.
The article underscores the urgent need for investment in innovative security solutions for AI and invites founders with expertise in AI infrastructure, governance, and security to contribute to addressing the evolving cyber threat landscape.
NVIDIA is seeking candidates to join their applied research team in developing tools for conversational AI systems, particularly NeMo Guardrails, an open-source toolkit for enhancing large language model-based conversational systems.
The role involves developing deep learning models and algorithms for dialogue systems, focusing on reducing harmful behavior and protecting against adversarial attacks. The ideal candidate holds a PhD or equivalent experience in Computer Science or related fields, with a strong background in deep learning, NLP, and Python programming. Experience in dialogue systems, cybersecurity, or open-source software projects is advantageous.
The salary range is $160,000 – $287,500 USD, with additional benefits and equity options. NVIDIA is an equal opportunity employer, promoting diversity and inclusivity in the workplace.
Hackers can read private AI-assistant chats even though they’re encrypted ArsTechnica, March 14, 2024 Despite efforts to encrypt communications, a newly developed attack has demonstrated the ability to decode AI ...
