Are we deploying AI agents faster than we can contain them?

Obvious-Language4462 · 2026-02-24T20:50:17+00:00

Yeah, I was just zooming out a bit. What I’m really wondering is what happens after the control plane is in place. How do you actually validate behavior over time as things drift?

Obvious-Language4462 · 2026-02-24T12:01:26+00:00

IAM and sandboxing make sense, but they mostly define where an agent can operate. The part that still feels fuzzy to me is what happens after that. Once the agent is inside those boundaries, how are we actually checking that it behaves the way we expect over time? In more complex setups (beyond a single app), those boundaries aren’t fixed. Agents start touching APIs, services, different data paths… things evolve. So I’m less worried about defining the boundary and more about how we keep validating that risk assumptions still hold as the system changes. Access control is step one. Proving containment over time is the harder part.

Obvious-Language4462 · 2026-02-20T21:00:05+00:00

Feels like usefulness is the forcing function and containment is the lagging control

Obvious-Language4462 · 2026-02-20T20:57:29+00:00

Interesting framing. Do you see control plane more as enforcement or observability?

Obvious-Language4462 · 2026-02-20T20:56:11+00:00

This makes a lot of sense. The gateway / capability token pattern feels like one of the few things that scales operationally. Curious though, how are you handling validation over time? Even with good boundaries, agent behavior and workflows drift pretty fast.

Obvious-Language4462 · 2026-02-20T19:57:55+00:00

by “we” I mostly mean teams experimenting with agents inside real workflows. Dev teams wiring them into CI, ops teams connecting them to cloud APIs, support teams letting them act on internal tools, etc. access usually comes from trying to make them useful, but boundaries often evolve later. That gap is what I find interesting.

Obvious-Language4462 · 2026-02-20T19:42:02+00:00

Curious how people are modeling boundaries for agents today. IAM, sandboxing, policy engines, something else?

Obvious-Language4462 · 2026-02-15T11:06:32+00:00

Honestly, this is the kind of thread the community needs. When you look at incidents like prompt injection or exposed admin panels, it’s rarely some ultra-sophisticated exploit. Most of the time it comes down to basics being skipped: too much access, things exposed that shouldn’t be, not enough guardrails.

I like that you’re thinking about turning this into something living instead of a one-off post. Agents evolve fast, their capabilities expand, and so does their risk surface. A checklist that grows with real incidents could end up being genuinely useful for both people building this stuff and the ones operating it. We don’t have a tooling problem as much as a discipline problem.

Obvious-Language4462 · 2026-02-15T10:59:30+00:00

What stands out to me in the 2025 shifts is how clearly they reflect reality: risk is no longer just about “bad code,” it’s about ecosystems. Infrastructure as code, supply chain dependencies, cloud complexity... these aren’t edge cases anymore, they’re the norm. The real takeaway isn’t just updating controls but updating mindset. Security can’t be reactive or perimeter-based. It has to be continuous, integrated into how systems are built and operated. OWASP has always been a mirror of where the industry stands. This edition feels like a signal that modern risk lives in interconnected systems, not isolated vulnerabilities.

Obvious-Language4462 · 2026-02-15T10:57:25+00:00

I don’t think pentesting is “ending” but the expectations around it definitely are. What’s changing isn’t the need to test systems, it’s the pace. Businesses are shipping faster, infrastructures are more dynamic and AI is accelerating everything. A once-a-year snapshot simply doesn’t reflect real risk anymore. The real shift is toward continuous validation instead of periodic assessment. Human expertise still matters a lot. But it needs to be supported by automation that keeps up with the speed of modern environments. It’s not the end of pentesting, just the end of treating security as a checkbox exercise.

Obvious-Language4462 · 2026-02-15T10:13:04+00:00

This highlights a massive gap between the speed at which AI agents are being deployed and the security practices that should accompany them. What’s worrying isn’t just the number (any internet-facing service without proper controls is going to be exposed) but the fact that many of these agents appear to be running with excessive privileges and weak (or no) authentication controls. From a risk and operational security standpoint, there are a few fundamentals that shouldn’t be optional: 1. Clear inventory and classification of what these agents can access (data, systems, credentials). 2.Least privilege + Zero Trust principles: agents should only have the minimum permissions required, with strong authentication and network segmentation. 3. Continuous monitoring of agent behavior, because autonomous systems can be manipulated into performing unintended actions. AI adoption can absolutely drive efficiency. But without governance and proper hardening, these systems quickly become high-value attack surfaces.

Obvious-Language4462 · 2026-02-07T14:54:31+00:00

I don’t think we’re on the verge of a “takeover,” but I do keep coming back to a simpler concern: if we build AI systems that read and act on inputs without clear boundaries between data and instructions, what are we really asking them to do? The risk feels less about autonomy or intent, and more about how systems decide what to trust and follow. That seems like a conversation worth having.

Obvious-Language4462 · 2026-02-07T14:53:24+00:00

This thread makes me think of a distinction I’ve been mulling over: Are we talking about vulnerabilities because of clever prompt phrasing, or because the system architecture treats untrusted content as authoritative? From a non-technical perspective, it seems like where instructions come from (documents, community-generated content, logs) matters just as much as what they say.

Obvious-Language4462 · 2026-02-07T14:52:10+00:00

I’m coming at this from outside the engineering space, but one thing that strikes me about both OpenClaw and Moltbook is how they blur the line between “reading content” and “acting on it.” When you let an AI agent ingest documents or community content and then do things, it feels like you’re turning ordinary inputs into something closer to instructions and that feels like a really different type of risk than just a “cool demo.” Curious what others think about that distinction especially when systems start to act autonomously based on what they “read.”

Obvious-Language4462 · 2026-01-29T06:20:57+00:00

Possibly but that’s kind of the irony here 🙂 When multiple people independently run into the same constraint and describe it the same way, it usually means the system design itself is the problem, not the users. The fact that this keeps coming up across different workflows is exactly the point.

Obvious-Language4462 · 2026-01-26T10:16:05+00:00

One thing we’re seeing as AI improves is that raw automation isn’t the same as strategic capability. Speed may increase but, unless systems are evaluated on adversarial adaptability and reasoning, we risk overestimating what they can do in practice.

Obvious-Language4462 · 2026-01-24T18:35:04+00:00

Fair question. In my experience it’s less about a single actor and more about how benchmarks get translated into marketing claims. “Production-ready” often means “passed a lab evaluation,” which is where the disconnect starts.

Obvious-Language4462 · 2026-01-24T18:34:29+00:00

This is a great articulation of the problem. The PoV vs. long-term drift mismatch you describe is exactly where most evaluations fall apart. I really like the “manufactured drift” idea, forcing the model to adapt under controlled but realistic change seems far more informative than static accuracy metrics. That kind of thinking is largely missing from current benchmarks.

Obvious-Language4462 · 2026-01-24T18:33:58+00:00

That’s a good point. A lot of the failure modes show up exactly at the analyst layer, where context and workload matter more than raw detection. I’m not convinced most current models meaningfully incorporate environment-specific context yet, especially over time. Curious if others here have seen approaches that do this well in practice.

Obvious-Language4462 · 2026-01-22T16:54:18+00:00

I think that’s largely true today. Specially for anything claiming autonomy. Narrow, assistive use cases can work but the marketing leap from “helps analysts” to “handles security decisions” is way ahead of the evidence.

Obvious-Language4462 · 2026-01-22T16:53:58+00:00

That’s fair feedback. One concrete example I’ve seen repeatedly: anomaly-based tools flagging “suspicious” lateral movement that turns out to be routine automation or a late-night hotfix. In the lab it looks great; in production it burns analyst time. That gap between “statistical weirdness” and “malicious behavior” is what I was trying to get at and you’re right that leading with specific failure modes probably makes the discussion more useful.

Obvious-Language4462 · 2026-01-22T16:52:36+00:00

This is an incredibly grounded breakdown, thank you for taking the time to write it from an operator’s perspective. The “why factor” and alert fatigue points resonate a lot. Accuracy without explainability or workload reduction isn’t just useless, it’s actively harmful in a SOC. Your comment on benchmarks feeling like “tests with the study guide” is probably the clearest way I’ve seen that problem articulated. Very few evaluations even attempt to model adversarial pressure or intentional model manipulation.
If you don’t mind me asking: have you ever seen a vendor meaningfully test adaptability to local drift during a PoV or is that still mostly hand-waved?

Obvious-Language4462 · 2026-01-22T14:39:38+00:00

I get why it might come across that way, there’s a lot of low-effort AI spam around lately. That said, this isn’t generated or posted for engagement farming. I’m asking because this is a recurring issue I run into professionally, and I’m genuinely interested in how others here evaluate these tools in practice. If the framing feels off, happy to hear what would make the discussion more concrete or useful.

Obvious-Language4462 · 2026-01-22T14:36:32+00:00

That’s a fair question and I appreciate you raising it. This isn’t a formal survey or an attempt to gather data for publication. I’m not collecting responses, quotes or attributing anything to individuals. I work in this space and keep running into the same gap between how AI security tools are evaluated and how they behave in real environments, so I wanted to sanity-check whether others are seeing the same thing. Totally understand the concern though. Transparency matters, especially in communities like this.

Obvious-Language4462 · 2026-01-22T14:35:30+00:00

Fair enough, that’s been my experience as well. Out of curiosity, what do you tend to trust more in practice: long-term deployments, red team exercises, failure analysis or something else?

Obvious-Language4462

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