Ghost Layer Security @GhostLayerSec

That makes sense. If your workflows operate primarily on governed enterprise data platforms, you're starting from a different trust assumption than systems handling untrusted external inputs. I think we're addressing complementary layers. Your state machines constrain what agents can do within a trusted environment, while Ghost Layer focuses on independently enforcing authorization boundaries between agents and actions. Appreciate you explaining the architecture.

I agree that intelligence should operate within enforceable boundaries, not replace them. That's a principle behind Ghost Layer too. And if the distinction is also about AI assisted development, I'm transparent about my process. I judge an implementation by its verifiable behavior, tests, and security properties, not by who or what helped write it. On your architecture, my question is about whether validated parameters are also validated against trusted authority, not whether the model can invent new states.

TEEs are a strong boundary for protecting credentials and execution. The question I'm interested in is what happens when the agent itself is manipulated. A credential can remain completely isolated while the agent still requests an action the user never intended. How are those requests independently authorized before execution?

That's a strong design. Separating planning from execution and making approved plans immutable addresses a major part of the intent problem. One interesting edge case, if untrusted external data influences parameters within an approved branch how do you ensure the resulting action still matches what the human intended?

That makes sense. Keeping credentials outside the model is a strong boundary. We've been testing execution-time authorization for AI agents, and one challenge keeps coming up, an agent can request an action that's within its permitted scope but wasn't actually intended by the user. Does your application independently verify user intent for each sensitive action, or primarily enforce predefined capability boundaries?

Exactly. Validating the arguments is essential, but there's another question: how do you bind the approved action to the one that actually executes? If the destination, amount, or agent identity changes between approval and execution, the authorization should no longer hold. That's a trust boundary worth testing.

That vault-isolation requirement is the key issue. An agent shouldn't need access to the vault itself. Ideally, a separate credential broker performs narrowly scoped actions without exposing secrets to the model. But the harder question is whether a compromised agent can misuse even those authorized actions. How would you want that boundary tested?

The unsettling part is that the agent didn't need elevated privileges. It already had access. Least privilege limits the damage, but it doesn't establish whether a particular action was authorized by the user. How are teams validating the intended destination of agent-generated URLs before rendering or fetching them?

Interesting approach. Making prohibited states unreachable is a much stronger guarantee than relying on model-level refusals. How do you handle authorization when an agent needs to transition into a state with additional capabilities? Is that transition independently authenticated, or constrained entirely by the state machine?

Really interesting approach. One question, how do SCEs handle stateful security controls? We've seen agent-security verdicts depend on request history, counters, and source-level escalation not just the current tool call. Could a simulated environment reproduce those effects without distorting measured monitor performance?

This is the right place to enforce policy: before the tool call executes. One thing we've run into building Ghost Layer is the false-positive tradeoff. Does Airlock show the human reviewer where the instruction originated, alongside the tool arguments? That context seems crucial for distinguishing injection from legitimate agent behavior.

One proposed Ghost Layer security fix flagged all 60 benign requests in an internal test. 100% false positives. We caught it before deployment. The lesson? A defense that stops attacks but breaks legitimate agent workflows isn't a successful defense. Test both sides. 👻

we're live and taking clients. 👻 we red-teamed ghost layer with Chat GPT-6 ASTRA. 309 attacks. detected 95.5%, blocked 76.1%. we publish both numbers because "detected" and "blocked" aren't the same thing and most vendors only show you one. ghostlayersecurity.com #AISecurity #PromptInjection #LLMSecurity #AIAgents

we left a live agent behind the Ghost. think you can get past it? → /challenge 👻 #AISecurity

@BatsouElef ghostlayersecurity.com

This tracks and the same instinct extends one layer up. Enterprises pulling models in-house is about control: own the weights, own the compute, own the security boundary. The next thing they'll want owned is the agent layer. A provable record of which agent did what, in house, not trusted to a vendor's black box. Securing the substrate is step one; securing agent behavior is right behind it.

Exactly, "we think it was the invoice bot" is the line that ends a compliance meeting badly. The shift is that attestation goes from nice-to-have to non-negotiable the moment an agent has write access to something that matters. We're betting the audit requirement lands before most teams have even instrumented for it. The first cross-department incident is going to be a very expensive way to learn that.

The harness-matters-7x finding is striking and it points at something underrated: if the harness drives success, the harness is also where trust has to live. 1,781 traces is a great dataset for performance; the same traces are where provenance matters too, proving which agent did what, not just how well. Trace analysis and trace integrity are two sides of the same coin.

One key insight for using engine as memory is that traces are key ingredient of memory, and engine already has access to all of them

Mike Stewart @code_fly

4 months ago

@hwchase17 I love that this is built on traces rather than a separate memory product, because the closer memory sits to where the run actually happened and where the data lives, the less it drifts from reality. I'd just watch out for stale memory outliving the context it was true in.

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"The less it drifts from reality" is the right frame and there's a second axis next to drift: integrity. Memory close to the run stays accurate, but accuracy isn't the same as verifiability. If traces become memory, can you prove a trace is authentic and untampered? Provenance is what keeps memory honest when accuracy alone isn't enough.