LLM Supply Chain Vulnerabilities
Malicious model files, poisoned datasets, and compromised ML packages are the new software supply chain frontier. Here is how these LLM attacks actually work.
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.
Malicious model files, poisoned datasets, and compromised ML packages are the new software supply chain frontier. Here is how these LLM attacks actually work.
A $60 domain purchase or 0.001% of training tokens can silently corrupt an ML model. Here's how training data poisoning attacks work and how to defend against them.
From Samsung's ChatGPT leak to RAG pipelines with no access controls, sensitive information disclosure is now a top LLM security risk. Here's how it happens and how to stop it.
LLM output that reaches a browser, database, or shell unvalidated can trigger XSS, SQL injection, or RCE. Here's how insecure output handling breaks AI apps.
LLMs hallucinate packages, vulnerability verdicts, and compliance summaries with total confidence. Here's where overreliance on AI outputs creates real security risk—and how to close the gap.
ChatGPT plugins, LangChain agents, and MCP servers have all shipped insecure plugin flaws exposing accounts and data. Here's how Safeguard defends against them.
LLM denial of service attacks exploit sponge prompts, unbounded generation, and denial-of-wallet loops to cripple AI systems without a single exploit.
Excessive agency turns a bad LLM output into an executed action. From Replit's July 2025 database deletion to Air Canada's chatbot ruling, here's what it is and how to scope it down.
Prompt injection is now OWASP's #1 LLM risk, and real incidents like EchoLeak and Slack AI prove it can mean zero-click data exfiltration. Here's how it works and what stops it.
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