Security Best Practices — MCP 2026-07-28
Versioned protocol guidance for authorization, token handling, SSRF and local-server risks. Start here when implementing an MCP service.
AI SECURITY RESOURCE DIRECTORY
Find research, practical guides, and source websites on prompt injection, MCP, agent security, and related topics.
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THE DIRECTORY
Versioned protocol guidance for authorization, token handling, SSRF and local-server risks. Start here when implementing an MCP service.
The protocol maintainers’ documentation is the starting point for MCP authorization requirements and transport security behavior.
A practical review checklist for MCP trust boundaries, authentication, tool access and deployment controls. Pair it with the protocol specification.
Community security guidance, risk taxonomies and implementation resources. Use the stated edition when comparing framework entries.
The published 2026 edition and its canonical source links. Use the edition label when mapping risks or comparing earlier assessments.
A concise practitioner framework for recognizing agent data-exfiltration risk. Useful for deciding which capabilities should be separated.
Trail of Bits demonstrates how malicious tool descriptions can influence an agent before invocation. A useful threat-modeling case study.
Tool-vendor recipes for testing MCP integrations, including tool poisoning and data-exfiltration scenarios. Adapt the examples to your deployment.
Explains why conventional injection analogies can mislead defenders, then discusses constraining agent capabilities and managing residual risk.
Practical testing recipes and open-source tooling documentation from the tool vendor, useful for constructing repeatable application evaluations.
Original vulnerability research and audit engineering from a commercial security firm, with technical examples and project links.
Search package security advisories and inspect identifiers, affected ranges and maintainer references; coverage extends beyond AI software.
Anthropic describes browser-agent defenses and its own evaluations. Useful for understanding layered controls and remaining risks, with vendor-reported results.
An adversarial AI knowledge base of tactics, techniques and case studies for threat modeling and investigation.
Model-provider research on agent behavior, evaluations and defenses. Read reported results as vendor experiments with stated conditions.
Wiz reports observations from its AI-service honeypots, including exposed MCP services. Treat the telemetry as this vendor’s sampled observations.
Structured open-source vulnerability records and package queries. Check ecosystem and version matching before applying a result.
First-party planning guides, lessons and tooling from Microsoft’s AI Red Team, useful for scoping and running assessments.
Microsoft’s planning guide covers assessment scope, people and process. Useful before selecting automated attack tooling or defining evaluation goals.
An early original paper demonstrating indirect prompt injection through retrieved content. Read the experimental setup and later revisions before generalizing.
NIST AI 600-1 organizes generative AI risks and suggested actions, including data privacy and information security, for governance planning.
Official examples for querying package versions or commit hashes. Useful when an agent needs structured vulnerability evidence rather than a web summary.
Independent practitioner explanations and experiments on prompt injection and agent design; distinguish demonstrations from general guarantees.
Cloud-security vendor research covering AI infrastructure exposures and exploit chains. Separate disclosed observations from product marketing.
Government guidance for secure AI development across design, deployment and operation, with practical lifecycle considerations.
Official risk-management publications and version updates. Useful for governance planning, rather than as a product certification.
Official API documentation for retrieving and filtering global advisories, with affected-package data and source references.
The project sponsor explains serialization risks and links the commissioned audit. Audit results apply to the reviewed scope and version.
Platform-maintainer documentation on model artifact scanning and repository security; useful when evaluating downloaded AI assets.
The maintainers’ structured ATLAS data supports threat-model lookups and agent workflows. Preserve technique identifiers and linked case-study provenance.
An OWASP-hosted editorial roundup linking reported incidents and research. The page states it is neither exhaustive nor an official OWASP publication.
NVIDIA’s official security bulletin repository provides maintainer notices and machine-readable records for its products, including AI infrastructure.
Find recent security preprints and original papers. A posting is not peer-review approval; inspect methods, revisions and linked artifacts.
OWASP application-security guides, including MCP, AI agent security and prompt injection. Each cheat sheet links practical controls and further references.
A Trail of Bits post presenting a critical examination of 1Password's AI patching benchmark and arguing that it is misleading.
Trail of Bits describes using AI agents to build auditing tools and formal models for a Miden VM security review. Useful for exploring AI-assisted audit workflows.
Wiz describes its model-input/output telemetry pipeline for detecting changes in agent intent. This is the vendor's engineering account; SecLLM has not independently evaluated its effectiveness.
Engineering article on the multi-agent pipeline and feedback loops Wiz used to turn a bucket scanner into an AI-powered data discovery context engine.
OWASP GenAI Security Project announces its 2026 Top 10 for LLM Applications, a new Agent Control Standard, and additional resources for securing generative and agentic AI.
Wiz Research analyzes NordStellar data to map credentials targeted by infostealer families and assess their potential impact across cloud, code, and AI environments.
Wiz reports a GitHub Actions injection flaw in Snowflake. Its correction says AI authorship of the vulnerable change is unconfirmed; Copilot Autofix made a separate fix in the same pull request.
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