Flat LAN Architecture & Zero Trust Gaps: A Security Assessment from Streamlit Testing to IoT Isolation
1. Background & Objectives
I’ve been working on an NLP-based fraud‐text detection project using Streamlit as the Web GUI. By default, Streamlit spins up a Python HTTP server on localhost—binding to 0.0.0.0 or 127.0.0.1 and opening a port so that anyone on our LAN can load the interactive interface in their browser. This rapid setup is ideal for quick prototyping, but once that server listens on the LAN interface, every internal user gains direct access. To determine whether I’d effectively left the metaphorical “front door” wide open, I set out to audit our network’s segmentation and routing isolation. My goal was to see how easily an attacker could pivot from that Streamlit entry point to other systems in our flat network—and, by extension, how IoT devices like network printers and IP cameras would be exposed under the same conditions.
2. Testing Workflow & Technical Details
| Step | Tool / Command | What It Tells Us |
|---|---|---|
| 1 | ipconfig /all |
Discovers the machine’s IPv4 address and subnet mask to map the Layer-3 network. |
| 2 | arp -a |
Lists all ARP cache entries (IP↔MAC). Hundreds of entries in one range confirm a flat Layer-2 domain. |
| 3 | tracert 10.x.x.x |
If only one hop appears before reaching the target, there’s no router in the path—a flat Layer-3 network. |
In Step 1, I relied on Windows’ built-in ipconfig /all—the fastest way to pull your IP, mask, gateway, and DNS servers without installing extra tools. Although I briefly considered Linux’s ifconfig/ip addr or fetching VLAN info via SNMP, those approaches either weren’t available or needed special permissions, so I stuck with what works out-of-the-box on Windows.
Step 2’s arp -a shows every neighbor that’s replied to an ARP request—exactly the hosts active in our broadcast domain. I did toy with running an Nmap ARP scan (nmap -sn) for richer data, but its longer runtime and tendency to trigger IDS alerts made me return to the no-frills, minimal-impact arp -a.
In Step 3, I ran Windows’ tracert to send ICMP echoes with progressively higher TTL values, revealing each routing hop on the path. I weighed using more advanced tools like pathping (adds packet-loss stats) or cross-platform mtr (live traceroute), but they required extra install effort. tracert gave me a crystal-clear yes/no on whether any router stood between me and my targets.
3. Test Results
The fact that your machine sits at *10.136.3. with a 255.255.224.0 (/19) mask tells us right away that you’re part of a very large subnet—one that spans 10.136.0.0 through 10.136.31.255, or over 8,000 possible hosts. In practical terms, this means that every device whose address begins with “10.136.” up through “10.136.31.” lives in the same IP neighborhood. There’s no automatic break at the “.3” boundary—your network’s mask has effectively flattened all of those /19 addresses into one single Layer-3 broadcast domain.
When you ran arp -a, seeing literally hundreds of MAC addresses all belonging to that same /19 range drives the point home at Layer-2. ARP only lists devices that replied to your ARP requests, so every single one of those MACs represents a machine on the same Ethernet or VLAN segment. If there had been private VLANs, separate subnets, or any kind of switch-level segmentation, you would have seen far fewer entries—or ARP entries only for the gateway’s MAC instead of every endpoint. Instead, your ARP cache looks like a “who’s who” of the entire /19, confirming there’s no hidden segmentation at the switch level.
Finally, your tracert to two peer addresses completing in a single hop seals the case at Layer-3. With only one hop showing—your target’s own IP—that means your packets never passed through a router or three-layer switch on their way there. In a truly segmented network, even devices on adjacent VLANs or subnets would require at least two hops (your gateway plus the destination’s local router). The immediate one-hop response is unequivocal proof that both the Layer-2 and Layer-3 fabrics are entirely flat, without any routing or ACLs standing between you and every other device in that massive /19 block.
Taken together, these results reveal an environment where every host, every printer, every camera—and yes, even that testing server bound to localhost—shares the same flat network space. There’s no logical checkpoint, no micro-segmentation, and no least-privilege enforcement baked into the network itself. This is precisely why lateral movement, ARP-based man-in-the-middle attacks, and broad scanning campaigns become so trivially easy for an attacker once they gain a foothold on any single machine.
4. Risks of a Flat LAN
A completely flat LAN essentially hands an attacker the keys to the entire network. Once they compromise a single host—say that Streamlit test server—they can immediately run ARP spoofing attacks using tools like BetterCAP or Scapy to poison the ARP tables of routers and switches. By impersonating the gateway, they intercept traffic from every machine on that /19 subnet, harvesting credentials and session tokens in cleartext or unencrypted protocols. At the same time, simple ICMP sweeps and TCP port scans (e.g., nmap -p 1–65535) let them discover open services—RDP, SSH, SMB, you name it—across thousands of addresses in seconds. Without VLANs or ACLs to block east-west flows, each discovered host instantly becomes a new target for exploitation or payload delivery.
Even absent an active breach, the mere presence of so many systems on one broadcast domain poses huge stability hazards. Misconfigured devices or malware-driven ARP storms can unleash a “broadcast tsunami,” saturating switch CPU and flooding every interface with gratuitous ARP responses. That flood not only cripples legitimate traffic—leading to a full or partial denial-of-service for critical applications—but also makes detection and incident response nearly impossible, as IDS and NDR sensors get buried under waves of repetitive broadcast packets. In a worst-case scenario, a single misstep—like an IoT camera stuck in a configuration loop—could paralyze your entire LAN.
Real-world breaches repeatedly show that lateral movement on a flat network is often the easiest path to full domain compromise. Once attackers gain a foothold, they spray credential-dumping tools (Mimikatz, CrackMapExec) across every machine, elevate privileges via well-known exploits (EternalBlue, PrintNightmare), then install backdoors like Cobalt Strike or custom malware to secure persistence. Because there’s no micro-segmentation or least-privilege firewalling between servers, desktops, and IoT endpoints, defenders have little chance to quarantine an infected segment before the attacker hops to the next victim. Breaking this cycle requires introducing VLANs, Layer-3 ACLs, or modern software-defined micro-segmentation so that every east-west hop demands explicit authorization, dramatically raising the bar for attackers to move laterally.
5. Zero Trust Gaps & Recommendations
Zero Trust fundamentally rejects the old castle-and-moat model where everything inside the perimeter is implicitly trusted. In our case, an attacker who breaches a single host on that flat /19 network could freely traverse between servers, workstations, and even IoT devices—because we’ve never segmented those core management, development, testing, and device networks into separate VLANs or subnets. Without Layer-3 ACLs enforcing “only these IPs on this VLAN may talk to that service,” every east-west packet moves unchallenged. We also haven’t implemented mTLS at the application layer or fronted our APIs with an API gateway that validates JWTs or OAuth2 tokens. As a result, any compromised credential or cookie grants unfettered access to every service in the same network, turning a single vulnerability or misconfiguration into a full-blown domain takeover.
To bridge this gap, we need a two-pronged approach: network-level micro-segmentation and application-level continuous verification. On the network side, carve out distinct VLANs—management, development, testing, and IoT—each bound to its own subnet and controlled by strict ACLs on core switches and firewalls. This ensures that, for example, your IoT VLAN cannot directly reach your critical server VLAN without explicit rules. On the application side, layer in a service mesh or SDN overlay (such as Istio or Cilium) so that every service-to-service call is wrapped in dynamic, mutual TLS with policies that verify both the identity and the health posture of each workload. By requiring mTLS and policy checks on every east-west transaction, you enforce “never trust, always verify” in real time—dramatically raising the bar for any lateral movement and aligning your architecture with true Zero Trust principles.
6. Extending to IoT Device Risks
IoT devices like network printers, IP cameras, and environmental sensors dramatically expand an attacker’s playground because they often ship with default or hard-coded credentials and multiple open protocols—HTTP for web interfaces, Telnet or SSH for management, MQTT for messaging, and SNMP for monitoring. With just a handful of well-known default passwords, attackers can quickly automate credential-spraying campaigns, taking over dozens of devices in minutes. Once they’ve breached a single camera or printer, they can pivot directly into the same flat /19 network, using that device as a foothold to scan for other vulnerable services. UPnP discovery and SNMP walks become their reconnaissance tools of choice, mapping out every device that responds—even those hidden behind barely monitored subnets.
Because most IoT firmware lacks automatic patching, known vulnerabilities—whether from 2018’s router bugs or last year’s CVE disclosures—remain exploitable indefinitely. An attacker who compromises one sensor can maintain persistence, quietly exfiltrate sensitive data (like scanned documents or video feeds), or enlist the device in a botnet to launch DDoS attacks from within your own network walls. And in a non-segmented environment, every probing request—benign or malicious—reaches every device instantly, supercharging the speed and scale of an attack. Without strict VLAN isolation, IoT-specific ACLs, or host-level agents that enforce encryption and authentication, these edge devices become the easiest path for attackers to breach deeper assets and stay hidden under the radar.
7. Holistic Detection & Defense
When it comes to detecting and halting lateral movement at scale, traditional perimeter firewalls simply don’t cut it. Modern Network Detection & Response (NDR) platforms—think Zeek or Suricata combined with NetFlow or sFlow collectors—continuously ingest telemetry from east-west traffic and apply both signature-based and behavioral analysis to spot things like ARP cache poisoning, mass SMB negotiation attempts, or a sudden surge in LLDP/CDP packets that suggest a rogue device scanning your network. These anomalies should be streamed into a centralized SIEM (eg. Splunk, ELK, QRadar) that incorporates User and Entity Behavior Analytics (UEBA), so you get real-time correlation across network logs, endpoint alerts, and authentication events. In practice, this means that when an attacker tries to pivot from a compromised Streamlit host into your file servers, you’ll see the indicators—unusual ARP requests, atypical SMB traffic patterns, or failed Kerberos authentications—triggering automated alerts or even policy-driven quarantine actions before they can hop further.
On top of that, enforcing true micro-segmentation ensures that no two workloads can communicate unless you explicitly allow it. Solutions like VMware NSX, Cisco ACI, Illumio, or eBPF-powered Calico/Cilium let you define per-workload firewall rules and inject sidecar proxies (as seen in service meshes like Istio) that wrap every service-to-service call in mutual TLS. These proxies verify both the identity and the health posture of each application before permitting traffic, effectively inserting an application-layer firewall between every pair of endpoints. Meanwhile, your Streamlit GUI, REST APIs, and IoT management portals should all require mTLS for transport encryption and validate OAuth2 or JWT tokens for user and machine authentication. When NDR, SIEM/UEBA, endpoint detection, and micro-segmentation work together, you convert what was once an open east-west “highway” into a series of gated checkpoints—each one demanding proof of identity and authorization—finally embodying the core tenets of Zero Trust.
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