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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.

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resk-logits Benchmark: Token-Level Filtering Latency

Measured CPU overhead from 100 to 10,000 patterns, with the exact command, raw JSON and the caveats we refuse to hide.

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LLM Guard Is Archived: Migration Guide and Alternatives

What the July 2026 archive actually changes, the four realistic options, and a one-line scanner migration path with the compatibility shim.

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Least Privilege for AI Agents: From Entitlement Mapping to Enforcement

Map every agent entitlement, derive capability masks by intersection, enforce at generation and tool-call time, isolate secrets and audit drift.

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MCP Security Mechanisms and Layers: A Technical Deep Dive

Six security layers for the Model Context Protocol — transport, authentication, manifest integrity, tool authorization, output handling and audit.

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Enterprise LLM Governance: From Policy Document to Enforced Control

Ownership, access control, PII handling, audit logs, model registry and policy-as-code — the parts of an AI governance policy that can actually be tested.

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LLM Security Monitoring & Observability: What to Log and What to Detect

Five telemetry planes, eight high-signal detections, privacy-safe logging and a 30-day rollout for LLM security monitoring.

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AI Penetration Testing: A Framework for LLMs and Agents

A repeatable six-phase method: scope, threat model, versioned corpus, agent and MCP testing, CI automation and remediation-focused reporting.

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TypeScript Secure Coding for AI Applications

Bundled API keys, unvalidated tool arguments, prototype pollution, SSRF and XSS — the TypeScript-specific mistakes in LLM apps, with fixes.

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Prompt Leaking: How System Prompts Leak and How to Stop It

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.

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ReskSafety: The Deployable Open-Source LLM Firewall

Meet 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.

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Self-Host an LLM Firewall with Docker in Minutes

A 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.

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Capability Bitmasks: Real RBAC for LLM Applications

Why 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.

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LLM Guardrails vs LLM Firewalls: Which One Do You Need?

Guardrails shape behavior; firewalls enforce boundaries. Where each fails, and how production systems combine both.

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Tool Permissions for AI Agents: Least Privilege in Practice

Agents with tools are privileged insiders. Capability bitmasks, generation-time gating, and post-call verification — applied step by step.

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Shadow Banning: Filter LLM Output Without Breaking the User Experience

Hard blocks crash conversations. Shadow banning makes harmful output statistically vanish — silent, invisible, jailbreak-resistant.

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8 Open Source Libraries to Secure Your LLM Applications

Discover the RESK open-source ecosystem: 8 free libraries to secure your LLM applications across Python, TypeScript, and Bun. Free on GitHub, PyPI, and npm.

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AI Red Teaming: The Complete Guide to LLM Security Testing

Master AI red teaming: adversarial testing techniques, prompt injection simulations, jailbreak detection, and how to harden LLM applications against real-world attacks.

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MCP Security: Protecting AI Agents from Model Context Protocol Threats

Understand 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.

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End-to-End Encryption for AI Conversations: The ReskCrypt Approach

Protect sensitive AI conversations with end-to-end encryption. Discover ReskCrypt's cryptographic approach to LLM data privacy and policy-based access.

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Securing AI Applications: Architecture Patterns That Work

Where 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.

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Prompt Injection Attacks: Case Studies and Lessons Learned

From 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.

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Zero Trust AI Security Architecture

Implementing Zero Trust principles in AI systems. Research on trust boundaries, continuous verification, and securing AI workloads in modern enterprise environments.

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Enterprise AI Governance Framework

Establishing robust governance frameworks for enterprise AI deployments. Research on risk management, compliance requirements, and organizational best practices.

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AI Security Best Practices: An Implementation Checklist

Best practices only work when they are sequenced. The production checklist for LLM deployments: identity, input, context, output, actions, monitoring, red teaming.

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LLM Access Controls & Monitoring: Best Practices

Per-team RBAC, token-level enforcement, audit logs and monitoring for enterprise LLM applications — with a deployment checklist.

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Implementing RESK-LLM Security Framework

Technical guide for implementing the RESK-LLM security framework. Step-by-step instructions and configuration examples for secure LLM deployments.

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