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    <title>FrostWolf AI Blog</title>
    <link>https://frostwolf.app/blog</link>
    <description>Research and guides on prompt injection, agent and tool security, guardrails, PII leakage, abliterated and uncensored models, and AI in offensive and defensive cyber.</description>
    <language>en</language>
    <item>
      <title>AI Agent Security: The Complete Guide</title>
      <link>https://frostwolf.app/blog/ai-agent-security</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/ai-agent-security</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>Agent and tool security</category>
      <description>AI agent security is protecting LLM agents that read untrusted content and take real actions. The threats, the architecture patterns that work, and a checklist.</description>
    </item>
    <item>
      <title>AI in Cybersecurity: How Attackers and Defenders Use LLMs</title>
      <link>https://frostwolf.app/blog/ai-in-cybersecurity</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/ai-in-cybersecurity</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>AI for cyber offense and defense</category>
      <description>How attackers and defenders actually use AI in cybersecurity: what threat reports show about misuse, where LLMs help defenders, and what is still hype.</description>
    </item>
    <item>
      <title>LLM Data Leakage: How Sensitive Data Escapes AI Apps</title>
      <link>https://frostwolf.app/blog/llm-data-leakage</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/llm-data-leakage</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>Data leakage and PII</category>
      <description>LLM data leakage is sensitive data escaping through an AI app's prompts, outputs, logs or tools. The seven paths it takes and the controls that close each one.</description>
    </item>
    <item>
      <title>LLM Guardrails: What They Are and How to Choose Them</title>
      <link>https://frostwolf.app/blog/llm-guardrails</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/llm-guardrails</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>Guardrails and defenses</category>
      <description>LLM guardrails are checks outside the model that inspect what goes in and comes out. The types, where they sit, open and managed options, and how to choose.</description>
    </item>
    <item>
      <title>OWASP Top 10 for LLM Applications, Explained</title>
      <link>https://frostwolf.app/blog/owasp-top-10-for-llm-applications</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/owasp-top-10-for-llm-applications</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>AI vulnerabilities</category>
      <description>The OWASP Top 10 for LLM Applications explained: all ten risks from prompt injection to unbounded consumption, with real examples and the controls for each.</description>
    </item>
    <item>
      <title>What Is an Abliterated Model? Refusal Removal Explained</title>
      <link>https://frostwolf.app/blog/what-is-an-abliterated-model</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/what-is-an-abliterated-model</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>Abliterated and uncensored models</category>
      <description>An abliterated model is an open-weight LLM whose refusal behavior was removed by editing its weights. How abliteration works, what it costs, and why it matters.</description>
    </item>
    <item>
      <title>What Is Prompt Injection? The Complete Guide</title>
      <link>https://frostwolf.app/blog/what-is-prompt-injection</link>
      <guid isPermaLink="true">https://frostwolf.app/blog/what-is-prompt-injection</guid>
      <pubDate>Thu, 29 Oct 2026 00:00:00 GMT</pubDate>
      <category>Prompt injection</category>
      <description>Prompt injection is an attack where text an LLM reads overrides the instructions it was given. How it works, real incidents, and how to limit the damage.</description>
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