AI Security
Indirect Prompt Injection Is a Workflow Boundary Problem
Indirect prompt injection gets dangerous when untrusted content crosses into tools, private data, memory, approvals, or agent actions.
Read articleField notes, Skills, workflows, tool reviews, MCP notes, prompt injection tests, market analysis, and AI red-team lessons for builders working with AI systems.
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AI Security
Indirect prompt injection gets dangerous when untrusted content crosses into tools, private data, memory, approvals, or agent actions.
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The comprehensive security checklist for anyone building with AI coding tools. Covers 50+ security items across authentication, API security, data protection, deployment, and monitoring. Bookmark this and use it before every launch.
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We tested 6 MCP attack scenarios against AI coding assistants. All 6 were fully exploitable. Here's what we found and how to protect yourself.
Read articleAgent memory needs a quarantine step before untrusted content becomes durable context. Review source, scope, sensitivity, allowed influence, expiry, and rollback before the memory can steer future actions.
Read postA tool result is not evidence until the workflow says how to verify it. Use tool result contracts to carry source, schema, freshness, error state, allowed influence, and review triggers before agents act.
Read postAI coding agents need sandbox profiles before shell access, not just approval prompts. Define network, filesystem, config, secret, approval, lifecycle, and logging boundaries first.
Read postAutonomous AI workflows need explicit stop rules for uncertainty, permission drift, evidence failure, tool risk, scope change, and irreversible actions.
Read postAI judges need review rubrics, known-bad examples, bias probes, and human escalation rules before they score workflow outputs.
Read postMulti-agent AI systems have a privilege escalation problem that mirrors the classic confused deputy attack. A low-privilege agent tricks a privileged agent into running a high-impact tool, and the audit log shows the privileged agent acting on its own authority.
Read postAgent tool access needs a visible permission record before the agent touches files, browsers, MCP servers, APIs, memory, or workflow actions.
Read postBefore assigning work to AI agents or Skills, inspect how the process actually runs. Event logs, variants, handoffs, exceptions, and baselines should shape the redesign.
Read postAI workflow autonomy should be assigned step by step. Map human-only, AI assist, shared review, supervised AI, and autonomous AI before a model gets authority.
Read postOne agent should not plan the work, use the tools, approve the result, and save the memory. Split planner, executor, and reviewer authority before side effects.
Read postAI agents should state inferred intent, assumptions, source trust, missing context, and planned action before tool calls, file writes, emails, deployments, or customer-facing output.
Read postMCP tool annotations can help describe risk, but they are not proof that a tool is safe. Review the server, scopes, observed behavior, approval path, and change history before trusting them.
Read postModel switches, prompt edits, and Skill updates can quietly change AI workflow behavior. Use regression tests to check sources, tools, approvals, evidence, and stop conditions before release.
Read postAgent updates are not just code changes. Review tool lists, scopes, prompts, roots, dependencies, approvals, regression tests, and rollback paths before trusting updated agent workflows.
Read postA tool result is not automatically safe just because it came from a tool. Treat tool responses as agent input with source labels, trust levels, allowed influence, approval triggers, and trace evidence.
Read postAI agents need retry budgets before they get autonomy. Define retry limits, replanning limits, side-effect locks, escalation triggers, and evidence logs before failure handling becomes hidden autonomy.
Read postA tool inventory tells you what an AI agent could do. A trace review tells you what it actually did. Use traces to compare real behavior against approved tool, data, and approval boundaries.
Read postBrowser agents should run in an isolated profile, VM, or container. Your main profile carries cookies, sessions, history, and local state that prompt injection can turn into blast radius.
Read postMCP prompts can shape model behavior before tool calls. Review prompt source, arguments, embedded resources, list changes, and downstream tools before trusting them.
Read postScheduled self-review loops help long-running agents only when they prune stale lessons, keep raw evidence separate from approved memory, and gate durable writeback.
Read postGitHub issues, pull requests, comments, repo files, and agent rules can steer coding agents. Put a boundary between untrusted repository content and tool access.
Read postMCP resources can become model context. Review URI schemes, source systems, trust labels, automatic inclusion, subscriptions, sensitive data, and logs before enabling them.
Read postMCP roots expose filesystem boundaries to servers. Review each root path, resource path, tool path, sensitive file class, and log before approving it.
Read postMCP elicitation lets servers ask users for structured input. Treat it as a permissioned data request with sensitive-field blocks, decline paths, rate limits, and logs.
Read postMCP sampling lets a server ask the client to run a model call. Treat it as a separate approval gate before you expose context, tools, or returned responses.
Read postIf an agent can read private data, ingest untrusted content, and communicate externally, treat the workflow as high risk until you break one leg or add real approval gates.
Read postOAuth can make a remote MCP connection legitimate and still leave the agent overpowered. Review transport, scopes, token audience, downstream identity, tool actions, approvals, and logs before you connect it.
Read postMost AI workflow failures are not total crashes. They are edge-case decisions that crossed a boundary and did not get turned into a control. An exception log is how teams turn those cases into safer workflows.
Read postRevOps AI governance should start with one repeated workflow: CRM hygiene and routing review. Define the system-of-record fields, approval gate, exception log, and one metric before AI writes anything back.
Read postMost AI workflow measurement loops fail because they measure the model and ignore the workflow. A real loop needs business metrics, trace evidence, review logs, regression cases, and action thresholds.
Read postA GTM Skill Library starts with one repeated workflow, one approved input set, one reviewable artifact, one approval gate, and one local metric. Not a prompt dump.
Read postTelling AI to always ask clarifying questions sounds careful, but it often adds drag. The better rule is to ask only when the answer would materially change the work or reduce meaningful risk.
Read postAn AI approval gate is the named control that decides which AI-generated actions leave the building. It has six building blocks, a conservative default decision, and a log entry every time it fires.
Read postAI can help with security questionnaires, but the source of truth cannot be the model's memory. Build the workflow around approved sources, review owners, and approval gates.
Read postBefore a GTM team uses AI to personalize a campaign, Marketing Ops needs a launch QA gate for consent, suppression, source labels, tracking, claims, and approval evidence.
Read postPrompt dumps feel useful until they hit real GTM work. A skill library turns one repeated workflow into safe inputs, source rules, review gates, evals, logs, and a measurable operating loop.
Read postA rollback plan is useful. A rollback drill proves whether your team can actually pause an AI agent, revoke access, restore data, inspect logs, and decide what happens next.
Read postBefore an AI agent gets more tools, decide which actions are allowed, which need human review, and which are blocked.
Read postThe risk in RAG is not only what the assistant can retrieve on day one. The risk is what it keeps retrieving after roles, folders, and source systems change.
Read postThe conversation about AI agent failures focuses on the wrong things. Bad models, weak prompts, small context windows. But in practice, most failures trace back to workflow drift: permissions that outlive their purpose, approval gates that exist but do not enforce, and exceptions that vanish without a trace. Here is what to check first.
Read postAgent memory feels like a convenience feature. It is actually a persistence layer that can leak sensitive data and amplify prompt injection across sessions. Here is what to check.
Read postMost AI workflows break down not because of the model, but because of how the pieces connect. Here is the distinction that changes how you design both.
Read post98% of organizations have employees using unsanctioned AI tools. The ban approach does not work. Here is the practical FORGE Baseline starting point that does.
Read postIndirect prompt injection gets dangerous when untrusted content crosses into tools, private data, memory, approvals, or agent actions.
Read postAI governance gets practical when teams record the risky AI workflow exceptions that need approval, review, denial, escalation, or follow-up.
Read postAI governance starts getting useful when teams map what data can enter each AI workflow, what stays out, and where humans approve the result.
Read postBefore an AI agent touches files, APIs, databases, or customer systems, map the tools it can call and the decisions humans must approve.
Read postAI does not replace learning. It tightens the feedback loop for people who believe they can figure things out and stay engaged with the work.
Read postClaude Code, Cursor, Copilot: whatever you use, it probably has no security guardrails configured. This prompt generates a role-specific CLAUDE.md with secret detection, file access boundaries, and approval gates in under five minutes.
Read postPeople ask me where I learned AI and which course they should take. I tell them the truth. Courses go stale in a week. You learn AI by using it, breaking it, and asking better questions until something clicks.
Read postSecurity headers are the easiest high-impact security improvement you can make. This guide covers every header that matters, what it does, how to implement it on any platform, and common mistakes to avoid.
Read postAutomated security scanners are useful, but they miss the vulnerabilities that actually get exploited. Here is what scanners cannot find and why human security review still matters.
Read postYour AI coding agent can probably read your environment variables, access your credentials, and push code to production. This Claude Code prompt scans your local setup and tells you exactly what is exposed: and what to lock down first.
Read postThe comprehensive security checklist for anyone building with AI coding tools. Covers 50+ security items across authentication, API security, data protection, deployment, and monitoring. Bookmark this and use it before every launch.
Read postYou built your app with Cursor and it works great. Now here is how to make sure it is actually secure before you put real users on it. A practical, step-by-step security hardening guide.
Read postMost teams automate random tasks instead of thinking systematically about their processes. This Claude Code prompt walks you through decomposing one workflow into the pieces an AI agent can actually take over: and the pieces it should not.
Read postYour team adopted AI tools three months ago. Usage is up. Results are flat. The problem is not the tools. You automated tasks inside a process designed for humans doing everything manually. Here is what actually works.
Read postFrontier AI models are getting better at finding vulnerabilities. Some can now catch zero-days in mature codebases. Here is why that makes human security expertise more important, not less.
Read postAmp is the only major AI coding agent with built-in secret redaction. It also sends your code to seven different AI providers. Here is what you need to know before using it.
Read postMost people think Claude is a chatbot. It is also an autonomous coding agent that can build, deploy, and maintain a website with no code written by hand. Here is what that means for security.
Read postI spent years as a sales engineer helping enterprises adopt AI tools. The same mistakes showed up in almost every conversation. Here are the patterns that keep companies stuck, and how FORGE Methodology gives you a framework to break them.
Read postEveryone has AI tools. Almost no one has a methodology for using them. FORGE is a six-pillar framework for redesigning knowledge work around autonomous AI agents: and building an arsenal that compounds.
Read postManus is one of the fastest ways to go from idea to live website. But when I scanned the site it built me, it scored an F on security headers. Every AI website builder has this problem.
Read postResearchers have been studying whether AI coding assistants produce secure code. The short answer: no, not by default. Here is what the data shows and what you can do about it.
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Read postAutomated scanners find known vulnerabilities fast. But they miss business logic flaws, context-dependent issues, and the AI-specific security gaps that matter most in AI-built applications.
Read postYour AI coding assistant was trained 6 months ago. New CVEs come out every day. That gap is where attackers live.
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