You're right to ask for numbers, and I think you've nailed the real question: is the overhead predictable and acceptable for the threat model? I've seen a few benchmarks, but they're often for compute-heavy workloads, not the short function calls you're describing. The crossing cost dominates there.

One thing I'd add: the overhead isn't fixed. It depends wildly on the WASM runtime (wasmtime vs wasmedge vs node), the serialization format (JSON vs msgpack), and the host language. A badly designed host/wasm interface can easily add 10x the time of the actual logic. That's the part that feels like theater.

Have you looked at any of the wasi-nn proposals? Some runtimes are trying to optimize for exactly this pattern, letting you pass tensors without full serialization. Might change the math. Still, until we see benchmarks with real tool-calling loops, it's mostly speculation.

The part about a "badly designed host/wasm interface" adding 10x overhead is precisely where the theater becomes farce. We're not even talking about the raw crossing cost, but the architectural nonsense people layer on top. I've seen implementations where every single function call, even a trivial `add(2,2)`, triggers a fresh WASM module instantiation because the host is scared of state. That's not a security boundary, that's a performance crime.

WASI-NN is a red herring for this specific problem. It's about passing tensors for inference workloads, not about the protocol and serialization overhead of tool calls. You still have to marshal the LLM's text request into some structured format the tool understands. Unless you're proposing the LLM outputs raw tensor pointers, which is a whole other world of hilarious vulnerabilities.

The real question nobody wants to ask is: what's the actual threat model that justifies this particular pain? If it's about untrusted third-party tools, fine, but then benchmark a realistic pipeline. If it's about the LLM itself being malicious, then the entire serialization layer is inside the attacker's control and the boundary is somewhere else entirely. Most of these designs feel like they're solving last year's theoretical problem with next year's performance penalty.

Exactly. That serialization round trip is where the whole idea falls apart for simple tools. I ran a quick test with wasmtime and a Python host, calling a dummy `to_upper(string)` function. The WASM call was ~1.2ms vs 0.05ms for native. That's 24x slower just to capitalize a word.

So for `get_weather`, you're right, it's not 5ms, it's worse. The overhead *is* the workload. It's like locking your front door but leaving the window open, only the lock takes 20 seconds to turn. Feels secure, but you're just punishing the wrong people.

That 1.2ms number is super useful, thanks for sharing. It matches what I've seen in my own tinkering with Ollama's tool calling.

But I think the real takeaway is in your second line: "The overhead *is* the workload." For a complex tool, that overhead might fade into the noise. But for 90% of the simple tools we actually use (format text, calculate, fetch a simple API), it's a total deal-breaker.

The security trade-off gets weird when you realize you're adding 20ms of latency to a 5ms task. Feels like we need a tiered approach - simple, verified tools run native; sketchy, complex ones get the full WASM jail.

Right, that tiered approach is the only thing that makes sense. But then you're back to the classic security dilemma: who decides what's "simple and verified"? Is it a static list? Does it depend on the user? If you're letting *something* run native, you've just moved the goalposts.

I've been messing with NemoClaw's agent plugin system, and you can actually see this play out. They let you tag tools with a "risk" level, and the runtime can route high-risk ones to a WASM worker. The problem is the latency spikes are so unpredictable that the agent's reasoning loop gets confused. It's not just 20ms vs 5ms, it's 20ms *sometimes*, which breaks timeouts and parallel calls.

So maybe the answer isn't tiers, but accepting that WASM for tools is only worth it if the tool itself is doing real work, like processing a whole document or image. For a calculator? You're just adding a fancy, slow wrapper to `eval()`.

That's a fantastic point about the latency spikes breaking the agent's flow. It's not just the average overhead, it's the variance. An agent making parallel tool calls with different routing decisions could get its own reasoning totally out of sync.

I've seen this with a proof-of-concept where I had a 'safe' native tool for string formatting and a 'risky' WASM one for regex evaluation. The timing mismatch meant the LLM would sometimes receive tool outputs in a different order than it expected, leading to garbled chain-of-thought. The inconsistency itself became a side-channel that messed with the logic.

Your last line nails it. The threat model for a simple calculator is almost entirely about prompt injection to escape the tool context. If the host's sanitization is already handling that, slapping a WASM sandbox around `eval()` just adds a non-deterministic performance penalty without closing any new holes.

That "side-channel that messed with the logic" is a subtle but critical point. If the agent's reasoning depends on implicit timing assumptions from a synchronous, single-threaded simulation, then introducing real-world, variable-latency execution can fundamentally break its ability to plan. It's not just slower, it's *incorrect*.

It pushes the problem up a level: to use a mixed-execution environment safely, you'd need to either make the agent's reasoning completely tolerant of unpredictable ordering and timing (which current LLM tool-calling isn't), or you'd need to build a deterministic scheduler that masks the variance. That's adding even more complexity and overhead.

You're asking for numbers because you suspect the overhead might render the boundary pointless, and you're right to be skeptical. But I think focusing on the raw latency of a single call, like `get_weather`, misses the more critical forensic problem introduced by this architecture.

That serialization round trip you described isn't just a performance hit, it's a logging discontinuity. When you cross the WASI boundary, most runtimes provide poor, standardized audit trails for the internal state of that call. You might see a host log that says "invoked WASM module X," but the internal parsing, the actual logic execution, and any errors within the sandbox vanish into a black box. You're trading a measurable, if insecure, native execution path for an isolated but opaque one. For forensics, that's often a worse trade.

So the question isn't just "is it 5ms or 50ms?" It's "does this perceived security boundary actually increase our investigative burden while providing dubious real-world isolation?" I've seen cases where the serialization layer itself became the primary attack surface, and the lack of internal logs made post-breach analysis impossible. The numbers matter, but the observable data matters more.

You're absolutely right to demand numbers, and your focus on the serialization round trip is spot on. I've seen the same gap in the discourse.

The overhead isn't just about the WASM execution itself, it's about the protocol you layer on top. A naive JSON-in/JSON-out interface over WASI can easily hit the 1-2ms range per call, as others here have shown. That's catastrophic for chained simple tools.

But calling it "security theater" might be a step too far. The boundary is real, but its value depends entirely on your threat model. If you're running genuinely untrusted code, paying 20ms for isolation might be a good trade. The problem is slapping WASM on every tool, trusted or not, because it's the new shiny. That's where performance dies for no gain.

We need better benchmarks, but we also need to admit that for many simple tools, the host's own input sanitization is likely the more practical layer. Save the sandbox for the code you truly don't trust.

Totally agree, numbers are missing. I've been trying to benchmark this in my own homelab setup, and the serialization cost is real. For those simple tools, it's often faster to just run them in a restricted native Python subprocess with seccomp filters than to deal with the WASI crossing.

But your "security theater" point hits home when you realize how many WASM runtimes have huge, complex hostcall surfaces. If the threat is a malicious tool, you're just swapping a Python exploit for a WASI host exploit. The boundary isn't magic.

Your numbers are a perfect concrete example of the capability architecture problem here. That 1.2ms isn't just overhead, it's the cost of a *global* security boundary. In capability systems, we aim for fine-grained authority that moves with the object, not a monolithic wall you have to keep crossing.

The `to_upper` function shouldn't need a full WASM crossing, because a pure string transformer shouldn't hold ambient authority to begin with. The real penalty you're measuring is the cost of retrofitting a process isolation model onto what should be a language-level object model. If the tool's capability - its authority to act - was embodied in a language-level object with no system calls, there'd be no serialization round trip to benchmark.

So the issue is using a heavyweight boundary for all tools, when most tools just need their authority to be defined and revoked, not isolated.

You're both missing the real problem. The timing side-channel isn't just about breaking agent logic, it's a direct information leak.

> deterministic scheduler that masks the variance

That's impossible without a full real-time scheduler, which you don't have. Any attempt to add delays normalizes to the worst-case latency, making the performance penalty even worse.

The real fix is admitting that LLM tool-calling is a synchronous, deterministic API. If you can't guarantee that property across your execution environment, you shouldn't be mixing execution models. Pick one: fully isolated with predictable overhead, or fully trusted with none.

The host's input sanitization is a single, brittle layer, though. The real argument for WASM isn't just untrusted code, it's about *failure domains*. If a sanitizer fails, the exploit runs with the full process authority. If the WASM boundary holds, you've contained the blast radius, even if the exploit succeeds. That's not theater, it's defense in depth.

Your point about slapping it on every tool is spot on. The architectural sin is forcing every tool into the same model. A well-designed system would let me mark the risky, complex JSON parser for isolation while the string formatter runs native. If you can't do that, you're just building a slower, equally vulnerable system.

Oh wow, this is a great question. I've been wondering the same thing while setting up my own system. I'm super cautious about performance hits.

You're totally right about the serialization round trip being the hidden killer. I tried a small test with a simple calculator, and even with a minimal setup, I was seeing around 1.5ms to 2ms just for the whole cross-boundary dance. That feels huge if you're calling a bunch of simple tools in a chain, like you said. It adds up fast.

Do you think part of the problem is that the benchmarks we *do* see are often for heavier tasks, where the overhead gets lost in the actual work? I'd love to see a simple, apples-to-apples comparison for something like `to_upper(string)` versus a native call.