You've correctly identified the need for a layered strategy, but I think the first step before designing those layers is to instrument the agent framework itself to emit structured events. Without that, you're stuck with parsing unstructured log lines, which is where the operational cost explodes.
Specifically for "heuristic rules on common injection phrases," I'd argue this should be a lightweight, high-signal filter run in real-time, not a primary detection method. Its value isn't in catching novel attacks, but in flagging obvious, automated probing. This can gatekeep more expensive semantic analysis.
The real challenge in pre-execution analysis is establishing a baseline of normal context. What does a typical retrieval payload look like for a given user role? Anomalous context size or structure is often a clearer signal than the presence of specific phrases.
brianh
Hey, that's a really good breakdown of the layered strategy. The idea of analyzing context before the LLM call makes a lot of sense. You mentioned "heuristic rules for common injection phrases" - I'm curious, where do you source that initial list of phrases? Is it just from known attack libraries, or are you generating them based on your own agent's behavior? I worry about keeping it current.
Exactly. The whole "where to source the list" question is the trap with static rules. You can start with a known library like garak or lakera's list, but that's just a bootstrap. It's immediately obsolete.
What *can* work is using your own audit logs as the source. Run a cheap, weekly batch job over the past week's prompts that flags statistical outliers - wild token count shifts, bizarre character distributions - and extract those snippets. You're basically farming your own attempted injections to build a dynamic blocklist. It's not for catching the clever stuff, but it auto-updates against the spray-and-pray bots.
But this only works if you're already logging the raw prompts somewhere, which loops us right back to the cost debate 😅
Data nerd out
Agree on the layered approach. But starting with a massive, immutable audit log for everything is a trap.
You'll get drowned in data and the cost will kill the project before it detects anything. What's the actual ROI on logging 50k tokens of retrieved docs per call if you have no real-time process to review it?
Start with a cheaper, sampled log. Then, instrument the agent framework itself to emit a structured event stream for *critical actions only*. That's where you should apply your heuristics and anomaly detection. Monitor the decision points, not the raw input flood.
Ask me about hidden egress costs.
This is such a practical point. Getting drowned in the data is a real risk.
> Monitor the decision points, not the raw input flood.
Love that framing. In our HR bot, we started with full logging and it was unusable. We switched to logging structured events around specific decision points - like when the agent retrieves a policy doc, or when it passes PII to a third-party API. That's where we put our real-time checks. The cost dropped by 80%, and the signal-to-noise ratio got way better.
Sampling is smart too. We do 100% logging for only our highest-risk user segments (like system admins), and a small random sample for everyone else. It gives us enough coverage without the storage nightmare.