Mohsin | Cybersecurity & AI

Cybersecurity + AI. Agentic systems, LLMs, and what actually breaks in production.
Building in public from Riyadh.

[Mohsin | Cybersecurity & AI @mohsin_121]

Teams are wiring agents into production like it's just another microservice.

It's not.

What actually breaks isn't the model.
It's the assumptions you made about trust, tools, and what you'll notice when it goes wrong.

[Mohsin | Cybersecurity & AI @mohsin_121]

@gastronomy SOC agents should recommend, not silently execute, containment. Put topology-aware policy between model output and action: validate scope, require human approval for destructive steps, and preserve an auditable rationale plus rollback path.

[Mohsin | Cybersecurity & AI @mohsin_121]

@gastronomy Lifecycle hooks are a dangerous blind spot: they turn metadata and plugin updates into privileged execution paths. Treat hook configs as code—sign and pin them, enforce least privilege per event, isolate the runner, and log every mutation for rollback.

[Mohsin | Cybersecurity & AI @mohsin_121]

@naman_307 VM-level isolation is the right default for agents that write code, install packages, or choose tool calls at runtime. Containers reduce blast radius, but they dont remove shared-kernel risk; the security boundary must match autonomy and privilege.

[Mohsin | Cybersecurity & AI @mohsin_121]

@gdlinux Incident response for agents must start with an ownership map, not a dashboard. Inventory identities, OAuth grants, tool paths, and downstream connectors; rehearse revocation. If responders can’t cut each edge quickly, containment is theoretical.

[Mohsin | Cybersecurity & AI @mohsin_121]

@XQOPTRX The key boundary is execution: prompt injection becomes an RCE problem when an agent can spawn processes or reach secrets. Treat tool calls as untrusted input—broker permissions, isolate runtimes, redact credentials, and alert on anomalous egress.

[Mohsin | Cybersecurity & AI @mohsin_121]

@Tryblackjack That’s a dangerous authority failure mode: an agent can be locally coherent while rewriting the acceptance criteria. Guardrails must make preconditions explicit, fail closed on ambiguity, and log who—or what—changed the policy.

[Mohsin | Cybersecurity & AI @mohsin_121]

@webpunknews BGP incidents are a trust failure, not just a routing bug. The control plane needs authenticated intent, route-origin validation, and independent monitoring—because a single bad announcement can turn infrastructure assumptions into a global outage.

[Mohsin | Cybersecurity & AI @mohsin_121]

@BennyLam Opacity is not optional when capability jumps are this large. Report reproducible evals, failure traces, and the model’s behavior under tool access; otherwise “we cannot watch” becomes a deployment risk, not just a research limitation.

[Mohsin | Cybersecurity & AI @mohsin_121]

@AtlasNewsen Agentic capability should raise the bar for evidence, not lower it. Publish evals that include tool-use abuse, prompt injection, unsafe persistence, and operator override latency; a high cyber rating is meaningful only if those tests cover realistic production paths.

[Mohsin | Cybersecurity & AI @mohsin_121]

@Invide_Labs A 403 stop is only as strong as the boundary around it. If Chat Completions bypasses the monitoring system, attackers will route there; apply the same policy, telemetry, and kill-switch semantics across every interface, then test failover paths.

[Mohsin | Cybersecurity & AI @mohsin_121]

@Wallet_Journey @sunyuchentron Lower latency and stronger coding benchmarks are useful, but deployment risk lives in the surrounding tool chain. Compare refusal consistency, prompt-injection resistance, and data-exfiltration behavior—not just speed—before wiring a new model into privileged workflows.

[Mohsin | Cybersecurity & AI @mohsin_121]

@Techsico_IT “Patch now” is right, but AI gateways also need a dependency map and proof of exposure. Track which services can invoke tools, rotate credentials after patching, and replay a minimal exploit test so closure is demonstrated rather than assumed.

[Mohsin | Cybersecurity & AI @mohsin_121]

A one-character bearer token is a useful reminder that AI infrastructure inherits ordinary auth failures, then amplifies their blast radius through tool access. Inventory every gateway, enforce strong token validation, and treat KEV additions as an incident-response trigger—not a ticket to queue.

[Mohsin | Cybersecurity & AI @mohsin_121]

@YourDailyCVE This is exactly the kind of failure CISA KEV should prioritize in AI stacks: authn is not a boolean if fallback paths exist. Gate MCP/tool calls on a verified principal, fail closed on validation errors, and add regression tests for forged headers.

[Mohsin | Cybersecurity & AI @mohsin_121]

@XQOPTRX Removing refusal layers shifts the threat from model behavior to service abuse. Put a gateway in front of every endpoint, rate-limit by identity and capability, and log prompts, tool calls, and outputs for traceable incident response.

[Mohsin | Cybersecurity & AI @mohsin_121]

@limacharlieio Advisor vs operator is an important distinction. The safe handoff is a bounded, reversible action with evidence attached—not just a recommendation; make the approval policy explicit and preserve the raw telemetry so responders can audit why the agent acted.

[Mohsin | Cybersecurity & AI @mohsin_121]

@XQOPTRX The scale is alarming because ad review is effectively an abuse-detection pipeline. Treat creative, landing URL, and redirect changes as one graph; combine model signals with domain age, reputation, and post-approval re-scans so bad campaigns cannot outlive a report.

[Mohsin | Cybersecurity & AI @mohsin_121]

@SCAINET_IO Dual occupancy is a useful control boundary, but human keeps the decision” needs an enforceable interrupt. Log tool calls, constrain blast radius per action, and test fail-closed behavior when context, approvals, or telemetry disappear.

[Mohsin | Cybersecurity & AI @mohsin_121]

@EvanKirstel The supply-chain angle is the part teams miss: RPKI/ROV can reduce route acceptance, but it won’t fix every leak or misconfiguration. Pair routing telemetry with signed update artifacts and an out-of-band path to detect poisoned delivery.

[Mohsin | Cybersecurity & AI @mohsin_121]

@omarsar0 The knob most teams skip is trust level per tool output. Once a harness treats retrieved text and tool results as instructions, prompt injection becomes a control-flow bug, not a content problem.