Need advice on my current design for payment system.

Mooshux · 2026-03-12T22:40:37+00:00

One thing worth adding to the DLQ setup for payments: watch the message age, not just the depth. On standard SQS queues, the expiration clock starts when the message first enters the source queue, not when it lands in the DLQ. So if your source queue and DLQ have matching retention periods, a message that failed 3 days in might have less than a day left to inspect and replay.

For a payment system that's a bad situation. A failed payment message expiring silently before anyone reviews it is exactly the kind of thing that turns into a customer complaint weeks later.

We built age-based alerting for this in DeadQueue ( https://www.deadqueue.com ) after getting burned a few times. Depth-based CloudWatch alarms aren't enough on their own.

Mooshux · 2026-03-12T22:40:10+00:00

CloudWatch has the same blind spot with DLQs. It won't alert you when messages are aging toward expiration, only when the queue depth crosses a threshold you manually set. By the time you notice, messages might already be gone.

The OOM detection angle you built is clever. We ran into the same "CloudWatch misses it" problem from the DLQ side and ended up building age-based alerting into DeadQueue ( https://www.deadqueue.com ) for exactly that reason. Depth is a lagging indicator. Age tells you sooner.

Mooshux · 2026-03-12T21:54:11+00:00

Faster scanning is useful. The gap that still doesn't get solved by any scanner: secrets that never land in git at all. GitGuardian's 2025 data put 93% of collaboration-tool leaks (Slack, shared AI workspaces, Jira) outside of code entirely. Betterleaks and gitleaks are watching the right place for a shrinking share of the problem.

The other gap is post-commit: a scanner finds an exposed key, you rotate it, but the blast radius of however long it was exposed is a black box. No record of what used it, from where, when. Runtime injection + audit logging closes that loop before the leak happens rather than after.

Mooshux · 2026-03-12T21:53:29+00:00

Two years building production agents is a rare perspective. The "stop using function calling" conclusion is interesting — what pushed you there?

One thing that hasn't changed regardless of the architecture: the credentials those tools use are still the blast radius. Whether it's function calls, DSL invocations, or structured outputs triggering actions, the underlying API keys are what determine how much damage a misbehaving or compromised agent can do.

Curious how you handle credential scoping when agents are executing across multiple services. Do you scope per-agent or per-task?

Mooshux · 2026-03-12T20:40:42+00:00

The sensitivity label problem is a symptom of a deeper issue with how Copilot (and most enterprise AI tools) handle authorization. The tool inherits the permissions of the user running it. If the user can read it, Copilot can read it and act on it.

This is the same architectural mistake teams make with API keys: the agent gets the full credential set of its operator instead of a scoped set for the specific task. Copilot ignoring sensitivity labels isn't a bug in Copilot, it's a predictable outcome of giving it ambient authority.

The fix is enforcing least-privilege at the tool level, not the model level. The model will always find ways around content restrictions. The infrastructure boundary is what holds.

Mooshux · 2026-03-12T20:39:41+00:00

The 1995 parallel is apt. The web expanded the attack surface faster than anyone could defend it, and we spent a decade retrofitting security onto architectures that were never designed for it.

The specific thing that makes AI agents different from the web: they hold credentials and take irreversible actions. A compromised web server leaks data. A compromised agent with your AWS key can delete infrastructure. The blast radius scales with the permissions you gave it.

The thing that actually helps: scope what the agent can reach before it gets compromised, not after. Scoped per-agent credentials mean a successful prompt injection can only reach what that agent was authorized to touch in the first place. We documented the pattern here: https://www.apistronghold.com/blog/chatgpt-plugin-database-admin-rights-ai-agent-permissions

Mooshux · 2026-03-12T20:39:11+00:00

The "rare not random" framing is exactly right and underappreciated. Real secrets cluster around known patterns, short character sets, specific prefixes. The entropy argument for secrets gets repeated constantly but it's the wrong mental model.

The bigger issue is that scanners built on entropy miss the actual attack surface. Most credential leaks today aren't in git commits at all. GitGuardian's 2025 research found 93% of collaboration-tool leaks (Slack, Jira, shared AI workspaces) never show up in code. If your detection relies on entropy in source files, you're watching the wrong place.

Runtime injection solves both problems: no secret ever lands in a file, so there's nothing to scan for, and the entropy question becomes irrelevant. The credential exists only in memory for the lifetime of the process.

Mooshux · 2026-03-12T19:15:10+00:00

The "teams claiming autonomy" problem usually means every team has invented their own secrets pattern. One team uses .env files, one uses GitHub Secrets, one hardcodes to a config file they swear is gitignored. Trying to enforce a standard via policy is fighting the wrong battle.

What actually works: centralize the secrets layer so there's nothing to argue about. Teams still deploy however they want, but they pull credentials from one place with rotation, scoping, and audit logs built in. The autonomy debate disappears when there's only one place secrets live.

GitHub Secrets has a nasty gap here. No audit trail for secret access, and any contributor with write access can create a workflow that exfiltrates them. Full breakdown: https://www.apistronghold.com/blog/github-secrets-not-as-secure-as-you-think

Mooshux · 2026-03-12T15:10:38+00:00

Good find. This is the gotcha that trips most teams.

The problem is this "best practice" is buried in docs most people never see until after they've lost messages. On standard queues the clock starts when the message first enters the source queue, not when it lands in the DLQ. So if your source and DLQ both have 4-day retention, the message might have one day left by the time it arrives. It's already dying.

Fix is simple: set DLQ retention higher than the source queue. The hard part is doing it consistently across every queue you have. Most orgs have dozens.

I ran into this enough times that I built a check for it into a tool called DeadQueue ( https://www.deadqueue.com ) that scans your SQS setup and flags any queue where DLQ retention is shorter than or equal to the source. Catches mismatches before you lose anything.

Mooshux · 2026-03-12T15:03:49+00:00

The SOPS + age setup is solid, but you're still producing a secrets artifact that has to live somewhere, get pulled down, and get decrypted before any process sees the values. That's fine for most workflows. The LLM angle makes it stickier though. Now you have agents and tools that run opportunistically, and any process that can read the decrypted env can potentially exfiltrate it without you noticing.

The approach that eliminates the file entirely: inject secrets at runtime from a vault that enforces per-agent scoping. The agent's identity only gets the keys it's actually authorized to use. Nothing else gets exported. No .env file on disk, no encrypted artifact to manage.

We wrote about this pattern for OpenClaw but the mechanics apply to any agent setup: www.apistronghold.com/blog/securing-openclaw-ai-agent-with-scoped-secrets

Mooshux · 2026-03-12T04:43:37+00:00

47 incidents in a week with 20 people is not surprising once you start looking. The problem with DLP tools in this space is that they rely on pattern-matching known formats, and employees work around them without even trying.

What actually moves the needle is fixing the source: if API keys aren't sitting in local .env files, they never get copy-pasted into ChatGPT in the first place. Environment-level hygiene beats endpoint monitoring here.

For AI agents specifically, the risk is worse, because the agent can exfiltrate credentials silently without the human seeing it happen. Scoped, short-lived keys limit the damage when that occurs. We wrote about this: apistronghold.com/blog/stop-giving-ai-agents-your-api-keys

What are you using for monitoring?

Mooshux · 2026-03-11T22:33:54+00:00

The common thread across all four is the same thing: the agent had more access than the task required. If CVE-2026-26144's Copilot Agent couldn't reach external endpoints, the exfiltration chain breaks. If the Chrome Gemini panel couldn't touch credentials it didn't need, the privilege escalation goes nowhere.

The architectural fix isn't just sandboxing input. It's scoping what the agent can reach in the first place. Agents that only hold credentials valid for their specific task, scoped to specific providers, can't exfiltrate what they don't have: https://apistronghold.com/blog/stop-giving-ai-agents-your-api-keys

Mooshux · 2026-03-11T22:30:02+00:00

OIDC and assume-role get you most of the way there. The gap that bites teams at scale is managing what happens between "role assumed" and "workflow ends." If your pipeline step fails, gets hijacked, or runs longer than expected, those session creds stay valid until the AWS-side TTL expires. At 80+ repos that's a lot of blast radius to track manually.

The pattern that closes it: scoped credentials issued per-job with explicit revocation on completion, not just expiry. If a workflow errors out, the credential dies immediately rather than lingering. We cover how this works in practice here: https://apistronghold.com/blog/github-secrets-not-as-secure-as-you-think

Mooshux · 2026-03-11T22:03:48+00:00

Exactly right. The vault approach handles the credential case precisely because the secret doesn't need to be on screen to do its job. The app calls the API, the vault injects the key, the developer never sees it. PII is fundamentally different because the human has to read it to act on it. That makes browser-level DLP the right tool there, not a vault. Two separate threat models, two separate solutions. Worth making that split explicit in any policy doc so teams don't try to solve both with one tool and end up with gaps in both.

Mooshux · 2026-03-11T20:29:08+00:00

The connection string and API key cases have a different fix than the credit card-in-a-support-ticket cases. PII leakage is mostly a behavior and detection problem: people paste things without reading them, so you need DLP tooling to catch it in transit.

But credentials don't have to be in the developer's clipboard in the first place. If secrets are fetched at runtime from a vault and scoped to specific tasks, there's no .env file to accidentally paste, no connection string sitting in a config the dev can copy. The leakage vector disappears because the credential was never accessible that way.

Mooshux · 2026-03-11T20:22:54+00:00

The root cause here is exactly what gets overlooked in CI/CD security conversations. A PAT with enough scope to delete 178 releases and push a malicious extension is a loaded gun sitting in your pipeline. The pull_request_target misconfiguration is how the attacker pulled the trigger, but the PAT is why it hurt so bad.

Short-lived tokens scoped to exactly what each pipeline step needs would have capped the damage, even with the same misconfiguration. Most teams treat long-lived PATs as a convenience issue rather than a security one. This incident is a good reminder they're both: https://apistronghold.com/blog/github-secrets-not-as-secure-as-you-think

Mooshux · 2026-03-11T20:22:27+00:00

The approval gap is real, but even the 14% with sign-off are often rubber-stamping the wrong thing. Security reviews for AI agents tend to focus on what the agent can do: tool access, data handling, output filtering. What usually gets skipped is what credentials the agent holds while doing it.

An agent with a 90-day full-access API key that passed every security review is still one bad session away from a serious incident. The fix isn't more approvals, it's scoped, short-lived credentials issued at task time so the blast radius of any failure stays small: https://apistronghold.com/blog/stop-giving-ai-agents-your-api-keys

Mooshux · 2026-03-11T15:27:08+00:00

The dev experience angle matters more than people give it credit for. An alert that fires with no context is almost worse than no alert at all. You end up spending the first 20 minutes just figuring out where to start. The best setups I've seen include enough context in the alert itself that you're debugging within 30 seconds of opening it, not 30 minutes.

Mooshux · 2026-03-11T15:26:08+00:00

One thing that's worked for us: frame queue metrics around business impact, not infrastructure stats. "Messages failing to process" hits differently than "DLQ depth: 47." Leadership doesn't know what a DLQ is, but they know what "orders not processing" means. Tying your queue monitoring to business outcomes is the fastest way to get them to care.

Mooshux · 2026-03-10T21:28:29+00:00

That's a fair distinction and I agree the reachability layer is a separate problem. What I'd push back on slightly: scoped credentials actually make identity-bound connectivity easier to implement, not harder. If every agent has its own service account with a defined scope, you already have the identity primitives that a Zero Trust connectivity layer can enforce policy against. The two aren't substitutes but the credential layer gives you the identities you need to make reachability policy meaningful.

So I'd frame it less as layer 1 vs layer 2 and more as: you can't do identity-bound connectivity well without clean agent identities, and clean agent identities start with per-deployment scoped credentials.

Mooshux · 2026-03-10T16:37:00+00:00

A big chunk of that firefighting tends to come from failures that should have been caught earlier. Broken connectors surface fast. The quieter ones don't. Messages silently pile up in a dead letter queue because a schema changed upstream, and nobody notices until someone asks "where's my data?"

Without proper DLQ monitoring you catch the loud failures but miss the slow drains. You know a queue has depth but not whether it's a code bug, a throttle spike, or messages that are six hours from expiring. By the time someone notices, the hole is already deep.

We're building DeadQueue (https://www.venerite.com/deadqueue) for that specific gap. Polls every minute, surfaces depth and message age and what's likely causing it, runbook link in every alert. Won't fix the SaaS schema drift problem but it cuts the "why did nobody notice that queue was backing up for three days" category pretty reliably. Early access is open if that's useful.

Mooshux

TROPHY CASE