ai-security
Safeguard articles tagged "ai-security" — guides, analysis, and best practices for software supply chain and application security.
593 articles
From CVE To PR: The Full Remediation Pipeline
A complete walkthrough of the modern remediation pipeline, from advisory ingestion through merged and deployed fix, with every stage that actually matters.
Injection Path Detection: Griffin AI vs Mythos
Injection vulnerabilities are not really about the sink. They are about the path from untrusted input to the sink. The path is where Griffin AI and Mythos-class tools diverge.
Fine-Tuning Poisoning Detection for Supply Chains
Fine-tuning inherits every problem of the base model and adds dataset provenance as a new one. Here is how detection actually works in practice.
Deepfakes and Social Engineering: The Human Layer of Supply Chain Attacks
AI-generated deepfakes are making social engineering attacks against software supply chains more convincing and harder to detect.
LLM-As-Judge Pitfalls In Security Evals
Using an LLM to score another LLM's output is expedient and dangerous. The judge has its own biases — ones that affect security evaluations specifically.
Griffin AI vs GPT-5: Enterprise Controls
Frontier models offer impressive enterprise features. Security programs need deeper controls than chat can provide—controls that live in the engine around the model.
Why Engine-Plus-LLM Beats Pure-LLM: Griffin vs Mythos
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
Task-Routed LLM Architectures For Security
One model for every task wastes budget on trivial work. Task-routed architectures match model capability to task requirements — the right lever for security at scale.
Windsurf vs Sourcegraph Cody: Security Comparison
A side-by-side security comparison of Windsurf and Sourcegraph Cody: data handling, agent scope, deployment models, and enterprise controls.
AI-Managed Security Services: What You're Actually Buying
AI managed security is sold as autonomous defense, but the honest version of the pitch is faster triage and drafted fixes with a human still signing off — worth knowing before you buy the marketing version.
Open Source AI Model Security: The Emerging Threat Landscape
As open source AI models proliferate, their security implications extend far beyond traditional software vulnerabilities. Model poisoning, supply chain tampering, and unsafe deserialization create new attack surfaces.
MCP Definition: What the Model Context Protocol Actually Is
The MCP definition in one line: an open standard that lets AI assistants connect to your tools and data through a single, consistent interface instead of a tangle of one-off integrations.
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