llm-security
Safeguard articles tagged "llm-security" — guides, analysis, and best practices for software supply chain and application security.
120 articles
How data poisoning attacks corrupt LLM behavior during tr...
A single expired domain and $60 can poison a training set. Here's how data poisoning attacks corrupt LLM behavior — and how Safeguard verifies training data before it ships.
How slopsquatting exploits AI-hallucinated package names
Slopsquatting attacks turn AI-hallucinated package names into real supply chain threats. Here's how it works, the numbers behind it, and how Safeguard stops it.
Using confidential computing to protect LLM inference and...
How hardware-based secure enclaves keep LLM prompts and weights encrypted even during active inference, and why confidential AI inference is reshaping AI compliance in 2026.
What AI red teaming is and how to run a structured exercise
A practical guide to AI red teaming: how to plan, run, and report a structured LLM red team exercise using a repeatable adversarial testing methodology.
How AI safety benchmarks and evaluations measure model risk
A concrete look at how AI safety benchmark evaluation, LLM safety scorecards, and capability testing actually measure model risk in 2026 — and where they fall short.
Explaining prompt injection attacks and why they're hard ...
Prompt injection attacks trick AI models into obeying attacker instructions hidden in data or user input, and there's still no complete fix.
How indirect prompt injection hides malicious instruction...
How attackers hide malicious instructions inside webpages, documents, and retrieved content to hijack AI systems — and why RAG pipelines are especially exposed.
Comparing LLM firewall and guardrail products for enterpr...
A vendor-by-vendor comparison of LLM firewall and AI guardrail platform options for enterprise deployment, with real strengths and limitations for each.
How RAG poisoning attacks manipulate retrieval-augmented ...
RAG poisoning attacks corrupt the external knowledge base an LLM retrieves from, turning trusted documents into vectors for misinformation and data leaks.
How to build an AI-specific incident response playbook
A step-by-step guide to building an AI incident response plan — covering scoping, escalation, detection, containment, and post-incident review for LLM and agent failures.
Prompt Injection Detection in Retrieval Systems
Indirect prompt injection arrives through your retrieval corpus, not your chat box. We cover the detection strategies that survive when attackers write your RAG content.
Prompt injection vulnerabilities in LLM applications explained
Prompt injection is OWASP's #1 LLM risk. See real CVEs like Vanna.AI's RCE flaw and how to detect and stop it.
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