Decepticon wraps web crawl output into LLM messages without neutralizing ChatML special-token literals.
Under BYOK, most self-hosted inference stacks do not filter those literals by default.
Read AdvisoryExpert insights on AI security, cybersecurity best practices, and cutting-edge security tools
Under BYOK, most self-hosted inference stacks do not filter those literals by default.
Read AdvisoryMeasured CPU overhead from 100 to 10,000 patterns, with the exact command, raw JSON and the caveats we refuse to hide.
Read ArticleWhat the July 2026 archive actually changes, the four realistic options, and a one-line scanner migration path with the compatibility shim.
Read ArticleMap every agent entitlement, derive capability masks by intersection, enforce at generation and tool-call time, isolate secrets and audit drift.
Read ArticleSix security layers for the Model Context Protocol — transport, authentication, manifest integrity, tool authorization, output handling and audit.
Read ArticleOwnership, access control, PII handling, audit logs, model registry and policy-as-code — the parts of an AI governance policy that can actually be tested.
Read ArticleFive telemetry planes, eight high-signal detections, privacy-safe logging and a 30-day rollout for LLM security monitoring.
Read ArticleA repeatable six-phase method: scope, threat model, versioned corpus, agent and MCP testing, CI automation and remediation-focused reporting.
Read ArticleBundled API keys, unvalidated tool arguments, prototype pollution, SSRF and XSS — the TypeScript-specific mistakes in LLM apps, with fixes.
Read ArticleHow 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, tool-call authorization, 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 ArticleWhere to place each control so it actually enforces: embedded libraries, an enforcement gateway in the request path, and action authorization for agents — with a worked request flow.
Read ArticleFrom Bing "Sydney" to EchoLeak and the Copilot Chat RCE: how five real prompt injection attacks worked, what each one teaches, and the defenses that map to each failure.
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 ArticleBest practices only work when they are sequenced. The production checklist for LLM deployments: identity, input, context, output, actions, monitoring, red teaming.
Read ArticlePer-team RBAC, token-level enforcement, audit logs and monitoring for enterprise LLM applications — with a deployment checklist.
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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