Expert insights on AI security, cybersecurity best practices, and cutting-edge security tools
How attackers extract system prompts and sensitive context — refusal loops, encoding tricks, context switching — and the defenses that work: canary tokens, output filtering, generation-time enforcement.
Read ArticleMeet ReskSafety: an OpenAI-compatible LLM firewall with RBAC capability bitmasks, editable filtering policies, multi-provider routing, and a full admin console. Try it live at demo.resk.fr.
Read ArticleA step-by-step guide to deploying ReskSafety — an open-source, OpenAI-compatible LLM firewall — on your own infrastructure with Docker Compose, from clone to enforced policies.
Read ArticleWhy LLM apps need per-user permission enforcement, and how 64-bit capability bitmasks enforce least privilege at the token level — so unauthorized content is never generated.
Read ArticleGuardrails shape behavior; firewalls enforce boundaries. Where each fails, and how production systems combine both.
Read ArticleAgents with tools are privileged insiders. Capability bitmasks, generation-time gating, and post-call verification — applied step by step.
Read ArticleHard blocks crash conversations. Shadow banning makes harmful output statistically vanish — silent, invisible, jailbreak-resistant.
Read ArticleDiscover the RESK open-source ecosystem: 8 free libraries to secure your LLM applications across Python, TypeScript, and Bun. Free on GitHub, PyPI, and npm.
Read ArticleMaster AI red teaming: adversarial testing techniques, prompt injection simulations, jailbreak detection, and how to harden LLM applications against real-world attacks.
Read ArticleUnderstand the security risks of the Model Context Protocol and how resk-mcp protects AI agents from tool-based attacks and prompt injection through tool outputs.
Read ArticleProtect sensitive AI conversations with end-to-end encryption. Discover ReskCrypt's cryptographic approach to LLM data privacy and policy-based access.
Read ArticleComprehensive guide to implementing robust security measures for AI applications. Covers threat landscape, secure architecture, and the RESK Security Framework for enterprise environments.
Read ArticleIn-depth analysis of prompt injection vulnerabilities in large language models. Research findings on attack vectors, detection methods, and effective prevention strategies.
Read ArticleImplementing Zero Trust principles in AI systems. Research on trust boundaries, continuous verification, and securing AI workloads in modern enterprise environments.
Read ArticleEstablishing robust governance frameworks for enterprise AI deployments. Research on risk management, compliance requirements, and organizational best practices.
Read ArticleEssential security practices for AI application development. Research-backed recommendations for secure coding, testing, and deployment of AI systems.
Read ArticleComprehensive security guidelines for Large Language Model implementations. Research findings on common vulnerabilities and protection strategies.
Read ArticleTechnical guide for implementing the RESK-LLM security framework. Step-by-step instructions and configuration examples for secure LLM deployments.
Read ArticleStay updated with the latest security insights and contribute to the advancement of AI security.
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