Blog 2

Discover key metrics, frameworks, and best practices for evaluating large language models (LLMs) effectively to ensure accuracy, ethics, and performance in 2024.

Discover 10 prompt injection techniques targeting AI, from prompt hijacking to payload injection, revealing vulnerabilities and emphasizing AI security measures.

Comprehensive LLM penetration testing checklist and top security tools to protect against vulnerabilities like prompt injection, data leakage, and adversarial attacks.

Explore the importance of data security, privacy, and ethics in AI, covering key regulations, risks, best practices, and the balance between innovation and trust.

Protect AI secures ML models with scanning, policy enforcement, and integration. Alternatives like Repello AI offer advanced threat simulation and real-time protection.

Explore HiddenLayer's AI security platform, its key features, gaps, and alternatives like Repello AI and Protect AI for comprehensive AI protection.

Explore Lakera AI's generative AI security features, key challenges, and alternatives like Repello AI and Protect AI for comprehensive AI protection.

Explore the top LLM Red Teaming platforms of 2024, designed to test and enhance the security of large language models against adversarial attacks.

Second part of the OWASP Top 10 Guide for LLMs explained for CISOs. Discusses Sensitive Information Disclosure, Insecure Plugin Design, Excessive Agency, Overreliance, and Model Theft. Also includes Security checklist for CISOs.

Discusses OWASP Top 10 for LLMs. Includes Prompt Injection, Insecure Output Handling, Training Data Poisoning, Model Denial of Service, and Supplu Chain Vulnerabilities.

Explore the importance of securing AI applications, common security risks, and best practices. Learn about Secure by Design, Zero Trust Architecture, and essential controls for safeguarding AI systems. Discover guidelines for role-based access control, continuous monitoring, and incident response planning to protect AI-driven solutions.

Learn the importance of securing AI models, common risks like data theft and manipulation, and key strategies such as data protection, access control, monitoring, and legal considerations. Explore best practices for maintaining AI security and preparing for potential breaches.

Explore key AI security vulnerabilities, including poisoned training data, supply chain risks, sensitive information disclosures, prompt injection attacks, denial of service, and model theft. Learn how these threats can impact businesses and discover strategies to strengthen AI security.

Understand AI jailbreaking, its techniques, risks, and ethical implications. Learn how jailbreak prompts bypass AI restrictions and explore strategies to prevent harmful outputs, ensuring user trust and safety in AI systems.

Explore the top 11 AI jailbreak communities, including Reddit, Discord, FlowGPT, and GitHub, where users share techniques and prompts. Learn about the ethical considerations and significance of these communities in the evolving AI landscape.

Discover the key security risks in Generative AI, including data poisoning, adversarial attacks, and model theft. Learn best practices for safeguarding AI models, protecting user privacy, and minimizing vulnerabilities in GenAI systems to ensure secure implementation.

Explore the latest jailbreak prompts for Claude 3.5 and ChatGPT in 2024, uncovering security vulnerabilities and the real risks these prompts pose to AI security.

Explore AI risk management, its importance for addressing security, ethical, and operational risks, and the frameworks that guide it, including NIST, EU AI Act, and ISO standards. Learn how companies can enhance security, regulatory compliance, and decision-making with automated AI risk management tools

Learn about AI red teaming, its role in enhancing AI security by identifying vulnerabilities, and how it differs from traditional red teaming. Discover common red team attacks on AI systems, key steps in the process, and best practices for effective AI red teaming.

Learn how Denial of Wallet (DoW) attacks can escalate costs for GPT-based applications and discover effective strategies like rate limiting, budget alerts, and user authentication to protect your LLM systems. Ensure security with a red-teaming assessment from Repello AI.

Discover the critical vulnerability of Retrieval-Augmented Generation (RAG) systems in this blog, where we demonstrate how small knowledge base manipulations can poison RAG pipelines. Learn how attackers can use gradient-based optimization and indirect prompt injection to compromise LLM safety and generate harmful outputs. Explore the importance of securing AI applications against such risks.

Explore the vulnerabilities of Meta AI's Prompt Guard in preventing prompt injections and jailbreaking attempts. Learn how adversarial attacks, such as Greedy Coordinate Gradient (GCG), can bypass LLM safeguards and misclassify harmful inputs as benign. Discover the importance of robust defense mechanisms for AI security.