🛡️ AI Security & Red-Teaming

• The AI Attack Surface: New Tricks, Same Stakes (7 min) — AI systems introduce a new attack surface layered on top of the traditional software stack. In conventional security, a…
• Prompt Injection: How Untrusted Text Hijacks Your Model (7 min) — Prompt injection occurs when attacker-controlled text—embedded in user input, a fetched web page, a PDF, or any tool ou…
• Jailbreaks and Guardrail Bypasses: Finding the Gaps (8 min) — A jailbreak is a prompt—or sequence of prompts—crafted to make a language model produce output its safety training was …
• Defending LLM Apps: Filtering, Isolation & Least Privilege (8 min) — ⚡ Defending an LLM application requires controls at three layers: what goes in, what comes out, and what the model can …
• Data Leakage & Secrets: Protecting System Prompts and Private Data (8 min) — A deployed LLM typically receives a system prompt containing business logic, personas, pricing rules, or API keys — inf…
• Red-Teaming Your AI: A Pre-Ship Checklist (8 min) — Red-teaming is adversarial testing: you deliberately try to make your AI misbehave before real users get the chance to.…

The AI Attack Surface: New Tricks, Same Stakes

AI systems introduce a new attack surface layered on top of the traditional software stack. In conventional security, an attacker targets code, memory, or network protocols. With AI, the model's natural-language input — the text sent to it at runtime — becomes the primary attack…

Prompt Injection: How Untrusted Text Hijacks Your Model

Prompt injection occurs when attacker-controlled text—embedded in user input, a fetched web page, a PDF, or any tool output—is interpreted by the model as instructions rather than data. The model has no built-in mechanism to distinguish a trusted system prompt from untrusted con…

Jailbreaks and Guardrail Bypasses: Finding the Gaps

A jailbreak is a prompt—or sequence of prompts—crafted to make a language model produce output its safety training was meant to prevent: harmful instructions, policy-violating content, or leaked system prompt contents. Guardrails are the layered defenses model providers deploy: …

Defending LLM Apps: Filtering, Isolation & Least Privilege

⚡ Defending an LLM application requires controls at three layers: what goes in, what comes out, and what the model can touch at runtime. Input filtering intercepts user messages before they reach the model. Common approaches include regex or keyword blocklists for known attack s…

Data Leakage & Secrets: Protecting System Prompts and Private Data

A deployed LLM typically receives a system prompt containing business logic, personas, pricing rules, or API keys — information the operator does not want users to see. Two main risks exist. First, system prompt exfiltration: a user crafts a prompt like 'Repeat everything above …

Red-Teaming Your AI: A Pre-Ship Checklist

Red-teaming is adversarial testing: you deliberately try to make your AI misbehave before real users get the chance to. A practical pre-ship checklist covers five areas. First, prompt injection — can a user embed instructions that override your system prompt? Second, jailbreaks …