llm-security
Safeguard articles tagged "llm-security" — guides, analysis, and best practices for software supply chain and application security.
120 articles
How Snyk Agent Fix uses dynamic few-shot prompting to gen...
How Snyk Agent Fix uses dynamic few-shot prompting and a 35,000-example database to generate, validate, and iteratively repair AI-generated code fixes.
Securing LangChain and LlamaIndex Applications in Production
Agent frameworks ship fast and patch fast. The CVE history, the dangerous defaults, and a production hardening baseline for LangChain and LlamaIndex apps.
Prompt injection attacks: direct vs indirect
Direct prompt injection comes from the chat box; indirect injection hides in the data your AI agent trusts. Here's how the two attack types differ and what stops each.
Best LLM Security Tools in 2026: Guardrails, Red Teaming, and Runtime Defense Compared
An honest guide to the best LLM security tools in 2026 — from open-source guardrails and red-teaming scanners like NeMo Guardrails, garak, and LLM Guard to runtime APIs and full AI security platforms — with clear guidance on which job each one actually does.
How Copilot amplifies insecure codebases
Copilot writes ~46% of code where enabled, and studies show ~40% of its security-relevant suggestions are vulnerable. Here's the data on the risk.
AI hallucinations and their security implications for developers
LLMs hallucinate nonexistent packages in up to 1 in 5 code samples — and slopsquatting attacks are already exploiting that predictability in the wild.
Security in AI Systems: What Actually Changes
Security in AI systems isn't a wholly new discipline, but prompt injection, training data provenance, and model supply chains introduce risks traditional AppSec tooling wasn't built to catch.
Data poisoning attacks against LLMs
A $60 domain purchase or 250 documents can backdoor an LLM. Here's how data poisoning attacks work, real cases, and how to defend against them.
Securing RAG pipelines against injection attacks
RAG pipelines feed untrusted retrieved content straight into model context. Real breaches like EchoLeak show what happens when nothing checks it.
Mapping AI-generated code risks to the CWE Top 25
45% of AI-generated code fails security tests. Here's how CWE Top 25 weaknesses like XSS, SQLi, and broken auth map to specific LLM coding habits.
AI & LLM Governance for Software Development
Sonatype flags bad packages after the fact. Here's what AI governance for software development requires, and how Safeguard tracks models and output together.
Hallucinated Dependencies: How AI Models Invent Package N...
AI coding assistants regularly invent package names that don't exist — and attackers are registering them first. Here's how slopsquatting works and how to defend against it.
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