AI Cybersecurity Companies and Vendors: The Landscape
AI cybersecurity companies split into three distinct groups doing very different work, and confusing them is the fastest way to buy the wrong tool.
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.
AI cybersecurity companies split into three distinct groups doing very different work, and confusing them is the fastest way to buy the wrong tool.
The difference between an engine-plus-LLM bug hunter and a pure-LLM one is not a tuning detail. It is a structural divide that determines whether the findings are usable.
When LLMs review PRs, triage issues, and fix builds, every commit message becomes attacker input. The concrete attack paths through GitHub Actions and what blocks them.
A threat model for sandbox escapes in Model Context Protocol servers, mapping attack surfaces from tool execution environments to host processes and shared state.
An attacker who can swap the model behind an API call can read every prompt and shape every response. The emerging trend in 2026 is model substitution as an attack class with its own techniques and disclosures.
Some tool calls cannot be undone. Out-of-band confirmation is the cheapest defense for that small set, and the most expensive thing to skip.
Pure-LLM vulnerability scanners hit production around 2024. By 2026 their failure modes are documented. Reachability remains the backbone — and the LLM is most useful on top of it.
A senior engineer's guide to training data poisoning defenses in 2026, from split-learning detection to provenance attestation and continuous pipeline monitoring.
Container security scanning tools find vulnerabilities in your images, layers, and dependencies before they reach production. Here is how the categories compare and what to look for.
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