Prompt Injection as a Supply Chain Risk in 2026
Prompt injection stopped being an LLM curiosity the moment agents started committing code. It is now a software supply chain risk and should be modeled as one.
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.
Prompt injection stopped being an LLM curiosity the moment agents started committing code. It is now a software supply chain risk and should be modeled as one.
A defender's glossary of AI security terms — prompt injection, model poisoning, AI-BOM, adversarial ML — with dated, real-world incidents behind each one.
AI source code analysis pairs LLMs with static analysis to cut false positives and speed triage -- but reachability data still decides what's real.
SPDX is the format auditors ask for, the format regulators reference, and the format most enterprise procurement teams standardize on. Griffin AI treats it as a first-class graph. Mythos-class tools treat it as a long document.
Gemini Code Assist makes developers faster. But faster is not safer. Here's how Griffin AI layers a security engine onto the same developer workflow.
Prompt injection is not a vulnerability that will be patched. It is what happens when a system cannot distinguish the instructions it is supposed to follow from the data it is supposed to process.
MCP server discovery turns a client connection string into a live capability graph. The protocol mechanics that make this convenient also widen the blast radius when discovery is spoofed, tampered with, or silently reshaped mid-session.
Taint tells you whether attacker data actually reaches a sink. Griffin AI propagates it; Mythos-class tools infer it. The difference shows up fast.
FedRAMP HIGH demands 421 controls with documented, continuous evidence. Griffin AI produces control-mapped records every day. Mythos-class pure-LLM tools cannot fill a 3PAO evidence package.
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