Can prompt injection make an LLM produce SQL injection?
Yes, if an app runs model-written SQL unchecked. Use parameterized queries, a read-only database role, allowlists, and treat model output as untrusted.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Yes, if an app runs model-written SQL unchecked. Use parameterized queries, a read-only database role, allowlists, and treat model output as untrusted.
Yes. Text a person cannot see can still be read by a model and change its output. Learn how indirect prompt injection works and how to harden pipelines.
A skill file is instructions, but it can bundle scripts an agent will run. Learn the risk, why untrusted skills are dangerous, and how to audit one.
Build a Node.js MCP server that fetches your current API reference on demand, so assistants stop quoting stale docs. Steps, pitfalls and safety checks.
Build a small MCP server in Python, test it in the terminal and wrap a client in a prompt loop. Learn the stdout rule that breaks stdio servers.
Security teams hold back on AI over false confidence, hard-to-measure risk, opaque behavior, new attack surface and data limits. Here is how to clear each.
Teams adopt AI in development only as far as they can verify its output. Provenance, review gates, scoped access and logs decide how much you can trust it.
AI-SPM means inventorying models, data, tools and agents and checking each against policy. Map it to the NIST AI RMF and OWASP LLM risks to start.
AI risk management uses the NIST AI RMF functions Govern, Map, Measure and Manage. See the benefits, the common challenges, and a first set of practices.
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