ratnaditya

28 ms·

1. 1.▲
   Prompt eval cues predicted refusal shifts across 32k LLM rollouts(medium.com)

   1 points by ratnaditya 5mo ago|0 comments
2. 2.▲
   Show HN: AgentWard – After an AI agent deleted files, I built a runtime enforcer(github.com)

   1 points by ratnaditya 7mo ago|1 comments
3. 3.▲
   by ratnaditya 7mo ago

   The kernel-level approach is the right answer for protecting the host from the agent — landlock and seatbelt give you deterministic enforcement that the LLM can't reason its way around. What I find interesting is the complementary laye
4. 4.▲
   by ratnaditya 7mo ago

   The hash-chained audit trail is a nice touch — append-only logs are underrated for agent governance. Curious how you handle the policy definition UX for teams without security backgrounds. JSON rules are expressive but require knowing what
5. 5.▲
   by ratnaditya 7mo ago

   The supply chain angle is the right framing — the typosquat example with mcp-servr-github is exactly the kind of thing that's hard to catch manually. One thing I've been thinking about in this space: static scanning at install tim