AI Security & LLM Security
AI Security is the discipline of defending AI systems against unique vulnerabilities like prompt injection and data poisoning.
LLMs blend data and instructions into a single stream, breaking traditional application security paradigms. Without deterministic proxy gates, AI systems act as massive attack surfaces for corporate data theft.
Multi-Hop Causal Traversal Engine
Explore how concepts dynamically feed into each other across 1-hop, 2-hop, and 3-hop transitive relationships. Click any node to navigate the causal highway.
AI Security & LLM Security
AI Security is the discipline of defending AI systems against unique vulnerabilities like prompt injection and data poisoning.
Direct Relationships (3)
Transitive Neighbors (Connected via Hop 1)
Extended Causal Ripple Effects
Latest Publications & Research Activity
Who’s Actually Responsible for Your AI Agents?
Salesforce and SAP are putting AI agents inside your workflows. Who tells them no?
How to Prevent Memory Loss in AI Applications
Frequently Asked Questions
Q:Why are LLMs inherently insecure?
Because they process instructions and user data in the same context window, allowing attackers to overwrite the original commands.
Canonical Specification Origin
The defensive architecture and governance protocols required to protect AI systems from prompt injection, data exfiltration, and malicious autonomous agent manipulation.
Corpus Interconnections
Richard Ewing artifacts developed around this canonical framework, including publications, execution tools, and diagnostic models.
External Adoption & Peer Citations
Documented instances where independent researchers, engineering teams, and publications have cited, implemented, or referenced this concept outside Richard Ewing’s ecosystem.
External Evidence: No independently verified references recorded yet.
This concept is part of Richard Ewing’s original baseline canon. External citations and implementations are added only upon rigorous empirical verification.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| Deterministic Governance | CIO.com | Editorial | ★★★★★ | Origin | Inspect ↗ |
Recommended Citation
Ewing, R. (2026). "AI Security & LLM Security." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/ai-security
@article{ewing_ai_security,
author = {Ewing, Richard},
title = {AI Security & LLM Security},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/ai-security}
}