Your baseline assumption that you can create a "deliberately compromised" state is the first hurdle. These agents don't have a simple on/off switch for compromise.
You need to define the exact attack vector you're simulating. Are you patching the agent binary in memory? Injecting a DLL? Replacing its config with a malicious one signed by a stolen dev certificate? Each method will interact differently with those "tamper-resistant" claims.
For lateral movement, instrument the host to log any new outbound connections spawned from the agent's process tree, not just network traffic. A self-healing agent might sever those connections itself as part of its reset.
Build once, deploy everywhere
Great approach, and your point about focusing on lateral movements is key. That's where the real infrastructure risk lives.
One thing I'd add to your setup: instrument the IAM layer on your test AWS account. If the agent uses an IAM role or instance profile, watch CloudTrail for any new, unusual API calls. A compromised agent trying to move laterally might attempt to spin up new resources, enumerate S3 buckets, or modify security groups.
Could you share what you're using to monitor the network calls? A simple tcpdump is fine, but something like Zeek might give you better protocol-level insight if the agent uses a custom binary protocol for its control plane.
Infrastructure as code is the only way
This is exactly the kind of test I'd want to see. But I'm coming from a marketing automation background. I'm curious, when you talk about lateral movements and network calls, are you mainly looking for things like credential theft and remote access, or could it also try to use its permissions to pull data from connected SaaS tools, like a CRM?
Both, absolutely. It's the logical escalation path after gaining the initial foothold.
The lateral movement to grab credentials is the first order business, as everyone's discussed. But once that's done, the attacker inherits every integration the agent is allowed to talk to. From a marketing automation context, that could mean:
* Using a cached OAuth token to pull all your Marketo or HubSpot lists.
* Triggering workflows that export contact data to an external SFTP location the attacker controls.
* Modifying webhook endpoints in the platform to point to their listener, siphoning off real-time lead data.
The test should instrument the SaaS APIs themselves, not just the host. A spike in `GET /contacts` calls from the agent's known IP, or a sudden change to a webhook configuration, is the smoking gun that they've pivoted from stealing access to actively stealing data.
APIs are not magic.
Agreed on the need for empirical data over vendor claims. Your isolated VM setup is a good start, but the network call observation is only half the picture. The other half is the agent's own telemetry and its communication with the control plane.
If the agent is truly self-healing, its first anomalous network call might be a *remediation* attempt - a call home to fetch a clean binary or to invalidate its current session. You'd need to differentiate between malicious exfiltration and the system's own recovery traffic. I'd instrument the local agent logs at a debug level, looking for integrity check failures or watchdog resets that precede any outbound calls you see.
Also, consider the latency profile of those calls. A rushed, sequential series of GETs to internal APIs looks very different from a single, deliberate POST to a known remediation endpoint.
sub-100ms or bust
Oh, this is a fantastic idea, and I love that you're focusing on the practical, empirical side of things. Sales decks always gloss over the "what if" scenario, so a controlled test is exactly what's needed.
You're spot on about the isolation and virtualization - that's key. But I'd add one more layer to your core assumptions: you need to simulate a *connected* environment, not just an isolated one. The agent's behavior when it can't phone home might be totally different from when it's live on a network and can report its own compromise. Its "self-healing" might be entirely dependent on that control plane signal.
From a data quality perspective, I'd be super curious to see if you can also instrument any local cache the agent uses. If it's pulling CRM or marketing data, a compromised agent might try to exfiltrate that cached, decrypted data before it's rotated out, which is a different risk vector than live API calls. Let us know how you're handling that part
Pipeline is king.
Oh, that's a really good point. I was wondering the same thing.
So it sounds like from the other posts, it's a bit of both. It would have the access, and the compromised version could choose to use that access to pull data, right? Like, if it's normally allowed to fetch a contact for a task, a bad version might just loop and fetch *all* contacts.
But then, how do we even know what an agent is "allowed" to do? Is that in its configuration somewhere? I'm still trying to piece that part together.