Cisco AsyncOS 0-Day: AI Triage for Email Gateways

A CVSS 10.0 zero-day that grants root-level command execution on email security appliances is the kind of incident that exposes a hard truth: most orgs still treat “patching” like a calendar task instead of an always-on risk decision.

Cisco’s alert about active exploitation of CVE-2025-20393 in AsyncOS (impacting Cisco Secure Email Gateway and Cisco Secure Email and Web Manager) is especially uncomfortable because these appliances sit in a high-trust spot. They handle the one thing everyone uses every day—email—and they often live close to the internet by design.

This post is part of our AI in Cybersecurity series, and I’m going to use this incident as a practical example of where AI actually earns its keep: detecting exposure before exploitation, prioritizing response when there’s no patch, and hunting for persistence at scale.

What happened with CVE-2025-20393—and why it’s a worst-case 0-day

Cisco’s advisory describes an improper input validation issue tracked as CVE-2025-20393 with a CVSS score of 10.0. The most important part isn’t the score; it’s the outcome: attackers can execute arbitrary commands as root on the underlying operating system.

Cisco also found evidence of persistence, which changes the playbook. When a threat actor plants persistence on an edge appliance, “apply mitigations and move on” stops being a safe assumption.

The exploitation prerequisites (and why they’re common in real life)

Successful exploitation requires two conditions:

• The appliance has Spam Quarantine enabled
• Spam Quarantine is exposed to and reachable from the internet

Spam Quarantine isn’t enabled by default, but plenty of teams enable it to reduce helpdesk load (“users can self-serve quarantine releases”) and then publish it externally for convenience.

Convenience is exactly what attackers count on.

Tooling used in the campaign: think access and staying power

Cisco attributed the activity to a China-nexus actor (UAT-9686) and observed post-exploitation tooling that’s very familiar in modern intrusions:

• ReverseSSH / AquaTunnel and Chisel for tunneling and remote access
• A log cleaning utility (AquaPurge) to reduce forensic visibility
• A lightweight Python backdoor (AquaShell) that listens for specially crafted HTTP POST requests and executes decoded commands

If you want a one-line takeaway: this wasn’t a smash-and-grab. It was an access-and-control operation.

Why email security appliances get hit so hard (and why AI helps here)

Email security appliances are attractive targets for three reasons:

1. They’re exposed: email has to accept traffic from the world.
2. They’re trusted: they sit inside “security” network segments and often have broad connectivity.
3. They’re overlooked: they aren’t a laptop, a server, or a cloud workload—so they fall into the cracks of many vulnerability management programs.

AI-driven security operations helps most in exactly these “crack” zones: systems where telemetry is inconsistent, ownership is unclear, and patch cycles are slower than attacker timelines.

The AI advantage: turning “asset + exposure + behavior” into real prioritization

Traditional vulnerability management asks: What CVEs do we have?

AI-driven risk triage asks:

• Is this asset internet-reachable right now?
• Is the risky feature enabled (Spam Quarantine)?
• Are there signs of exploitation behaviors (new tunnels, weird POST patterns, log tampering)?
• Does this device normally talk to these destinations and ports?

When there’s an unpatched zero-day, “prioritization” can’t be a weekly meeting. It has to be an automated decision loop.

No patch yet: how to use AI for rapid mitigation and detection

Cisco’s guidance (secure config, limit internet exposure, firewall allowlists, separate interfaces, monitor logs, disable HTTP for admin portal) is solid. The problem is speed and scale—especially in large enterprises with multiple appliances, HA pairs, or regional deployments.

Here’s how I’ve seen teams use AI in cybersecurity to close the gap when the patch isn’t available.

1) AI-powered exposure management: find the truly reachable systems

The fastest win is answering: Which appliances are exposed in the exact exploitable way?

An AI-powered exposure workflow can correlate:

• Asset inventory (CMDB, NAC, cloud/virtual appliance lists)
• Network edge data (ingress rules, NAT mappings, reverse proxies)
• Service detection (what’s actually listening externally)
• Configuration drift signals (recent changes to quarantine portals)

That correlation is where humans lose time. AI doesn’t “magically know,” but it can join the dots across messy data sources and surface a short list: these specific gateways are reachable and match the exploitation prerequisites.

2) Behavioral detection: identify exploitation even when IOCs lag

IOCs are useful, but attackers rotate infrastructure and modify payloads. For incidents like this, AI-assisted detection should focus on behaviors that are hard to hide:

• Unexpected creation/execution of tunneling binaries (ReverseSSH, Chisel) on an appliance
• New outbound connections from a mail security device to unusual ASNs/regions
• Anomalous spikes in HTTP POST requests to quarantine endpoints
• Evidence of log deletion, truncation, or irregular log gaps

A practical stance: treat your email security gateway like an endpoint from a detection perspective. If your tooling can’t do EDR on the appliance, you can still do strong network- and log-based detection with anomaly models.

3) AI-assisted incident response: speed up scoping and “blast radius” answers

When leadership asks “Are we affected?”, the honest answer is usually “We’re checking.” AI can shorten that uncomfortable window by automating:

• Timeline building (first seen suspicious traffic, first outbound tunnel, config changes)
• Entity clustering (which appliances show similar patterns)
• Triage summarization (what happened, on which device, with what confidence)

This matters because the containment decision is binary and urgent: is the quarantine feature still exposed, yes or no? AI is good at rapidly producing that operational truth.

What to do this week: a zero-day response checklist for AsyncOS appliances

This is the action plan I’d want a security team to execute immediately after an advisory like this—especially given the date: mid-December, when staffing is thinner and change windows get weird.

Immediate containment (same day)

1. Identify all AsyncOS-based appliances (physical and virtual) across regions and environments.
2. Verify whether Spam Quarantine is enabled and on which interface.
3. Remove internet reachability to Spam Quarantine:
   • Restrict by firewall policy to trusted IPs/VPN only, or
   • Fully disable external exposure until a patch exists.
4. Disable unnecessary services and close any management ports exposed to the internet.

Detection and hunting (24–48 hours)

• Hunt for indicators of tunneling behavior:
  • New/rare outbound destinations
  • Long-lived outbound connections
  • Traffic patterns consistent with reverse tunnels
• Review web logs for unusual request patterns to quarantine endpoints (volume, user agents, repetitive POSTs).
• Check for artifacts consistent with persistence (unexpected scripts, cron-like behaviors, new binaries).

Eradication decision (don’t sugarcoat this)

Cisco’s position is blunt: if compromise is confirmed, rebuild is currently the only viable option to remove persistence.

That’s painful, but it’s also the correct mindset for an edge appliance that has run attacker code as root. If you can’t establish trust, you rebuild.

The KEV deadline is the point: automated patch prioritization is now table stakes

CISA added CVE-2025-20393 to the Known Exploited Vulnerabilities (KEV) catalog, and federal agencies have a mitigation deadline of December 24, 2025.

Even if you’re not in the federal world, KEV is a strong signal of what matters right now. The operational lesson is bigger than Cisco:

• Your patch backlog will always be large.
• Attackers only need one path.
• AI-driven patch prioritization is how you decide what gets fixed first based on exploitation evidence and real exposure.

A simple prioritization formula that works well in practice:

• Exploitability (active exploitation, KEV listing)
• Exposure (internet-reachable, feature enabled)
• Privilege/impact (root/system, lateral movement potential)
• Observed behavior (anomalies, tunneling, persistence hints)

AI helps by continuously recalculating this as new signals arrive.

VPN credential campaigns are the other half of the story

The same news cycle included reporting of large-scale credential-based login attempts against enterprise VPN infrastructure (including Cisco SSL VPN and Palo Alto GlobalProtect). That activity wasn’t exploitation of a vulnerability—it was automated credential probing.

Pair that with an email gateway 0-day and you get a realistic December threat model:

• Attackers push on edge services
• They mix exploitation (0-days) with access economics (credential stuffing)
• They prioritize tools that create durable access (tunnels, backdoors, log cleaning)

AI helps here too, but only if it’s aimed at the right job: detecting abnormal authentication patterns, blocking scripted login attempts, and correlating identity telemetry with network behavior.

A better way to approach email gateway security in 2026

If this incident made you uncomfortable, good. It’s a reminder that “security appliances” are still computers, still exploitable, and often more operationally fragile than standard servers.

Here’s the stance I recommend going into 2026:

• Treat edge appliances as high-value endpoints.
• Assume “unpatched window” is normal, and design controls for it.
• Use AI-driven threat detection to catch behaviors, not just known signatures.
• Invest in AI-powered exposure management so you can answer, fast, what’s reachable and risky right now.

If you want to pressure-test your current program, ask yourself one question: when the next CVSS 10.0 zero-day hits an edge appliance, will you find your exposed systems in minutes—or in days?