Ping Packet Storage: AI Security Lessons for Startups

ICMP “ping” wasn’t designed for storage. That’s exactly why pingfs is worth your attention.

pingfs is a small open-source experiment that mounts a Linux filesystem and stores file data inside ICMP Echo packets that travel across the network. It’s weird, clever, and wildly impractical for production. And that’s the point: in a startup and innovation ecosystem, these “wrong” ideas often reveal the right security questions—especially when you’re building AI products that depend on data pipelines, telemetry, and automated security operations.

This post is part of our “साइबर सुरक्षा में AI” series, where we look at how AI improves threat detection, attack prevention, and security operations. Here we’ll use pingfs as a lens to sharpen your thinking about data exfiltration, covert channels, anomaly detection, and what AI can (and can’t) do when attackers stop playing by the usual rules.

What pingfs really shows: data can hide in “normal” traffic

Answer first: pingfs demonstrates a classic security reality—any protocol can become a data transport, even ones we treat as harmless diagnostics.

ICMP Echo (ping) is typically allowed through parts of a network because it helps with reachability checks and troubleshooting. pingfs flips that assumption: it uses raw sockets + FUSE (so it needs root privileges) to send and receive ping packets whose payload contains filesystem blocks.

A few concrete details from the project:

• It’s a Linux-focused filesystem implemented in C.
• It relies on FUSE to present a mount point.
• It uses ICMP Echo packets (IPv4 and IPv6 supported) as the storage medium.
• It supports basic file operations (create, read, write, rename, chmod) but not directories or timestamps.
• The README itself warns performance is low and data loss can happen, especially on LAN hosts.

So no, you’re not going to run your startup’s customer data on ping packets.

But as a security and AI signal: it’s gold.

Because the core idea—data embedded inside permitted traffic—is exactly how many real-world attacks work.

Why startups should care (even if you’ll never use pingfs)

Startups building AI products often inherit a dangerous mental model:

“If we block uploads and restrict file transfer tools, we’ve reduced data leakage risk.”

Not enough. Data can move through:

• DNS queries
• HTTP headers
• TLS SNI / certificate fields (in some scenarios)
• Collaboration tooling metadata
• Logging pipelines
• And yes, ICMP

If your AI stack ingests sensitive prompts, embeddings, training snippets, or customer telemetry, you need to assume that non-obvious egress paths exist—sometimes by accident, sometimes by design.

ICMP-based storage as a blueprint for covert channels

Answer first: pingfs is a clean, understandable example of a covert channel: sending data through a channel not intended for that purpose.

In security, covert channels matter because many orgs focus on controlling “big obvious” paths (cloud drives, email attachments) while ignoring protocol-level abuse.

pingfs also clarifies an uncomfortable truth about policy:

• If your network allows pings broadly, you’re already allowing a form of data movement.
• If your monitoring treats ICMP as low priority, you’ve created blind spots.

Practical parallels to modern data exfiltration

Attackers rarely copy pingfs exactly, but the pattern is familiar:

1. Establish a channel that blends in (allowed protocol, “boring” traffic).
2. Chunk the data (small payload pieces).
3. Add just enough reliability (retries, sequencing, acknowledgements).
4. Reassemble on the other side.

That “chunking + sequencing + reassembly” is basically what any transport system does—pingfs simply does it where you don’t expect.

For AI-first startups, this is especially relevant because your crown jewels might not be a file named secrets.zip. It might be:

• a vector database snapshot
• system prompts and tool instructions
• private evaluation datasets
• customer conversation logs

Treat those assets like source code: monitor their paths, not just their storage.

Where AI helps: detecting “weird, low-and-slow” network behavior

Answer first: AI improves security here by spotting patterns humans and static rules miss—especially low-and-slow exfiltration and protocol misuse.

Traditional monitoring might only alert on:

• high bandwidth spikes
• known bad IPs/domains
• signatures of common malware

But covert channels often aim to be:

• small packets
• consistent timing
• benign destinations
• “normal-looking” traffic classes

AI-driven anomaly detection signals that matter for ICMP misuse

If you’re building (or buying) AI threat detection, make sure it can model behavior like:

• ICMP volume anomalies: a host that suddenly sends 10× more Echo requests than its baseline
• Payload size distribution: unusually large or unusually consistent ICMP payload lengths
• Inter-arrival timing: packets emitted at machine-regular intervals (a common automation smell)
• Peer diversity: pinging many hosts or a rotating set of IPs without operational justification
• ICMP error/response patterns: asymmetric request/response ratios, repeated retries

A useful stance: ICMP isn’t suspicious; patterns are.

“But we already block ping” isn’t a strategy

Many enterprises do block ICMP at the perimeter. Many don’t. Startups often can’t, because they rely on managed networks, third-party providers, or distributed customer environments.

A better approach is layered:

1. Egress controls: allow ICMP only where needed, from known segments.
2. Visibility: log ICMP metadata at minimum (counts, sizes, destinations).
3. AI baselining: learn per-host and per-service norms.
4. Automated response: rate-limit, isolate endpoint, or require step-up auth for sensitive systems.

This is exactly where “साइबर सुरक्षा में AI” becomes practical: AI isn’t replacing firewall rules; it’s helping you manage the messy edges where rules can’t be perfect.

Startup playbook: turning weird experiments into product insight

Answer first: the value of pingfs for startups is not the filesystem—it’s the mindset: stress-test assumptions, then design AI controls around the failure modes.

I’ve found that early-stage teams ship faster when they decide what they won’t handle. Security doesn’t get that luxury. Attackers choose the edge cases.

Here’s a lightweight playbook you can run in a week.

Step 1: Map your AI data flows like an attacker

List your AI-adjacent assets and paths:

• data sources (support tickets, app logs, PDFs, voice)
• processing (ETL jobs, prompt assembly, embedding pipelines)
• storage (object stores, vector DBs, caches)
• outputs (APIs, dashboards, agent actions)

Then ask one blunt question: Where can data leave the system if “normal” channels are blocked?

That’s where covert-channel thinking helps.

Step 2: Decide what “normal ICMP” means in your environment

Most teams never define this.

Pick 3–5 metrics you can actually measure:

• ICMP packets per host per hour
• top ICMP destinations
• average payload size
• percentage of ICMP allowed vs blocked

Even if you don’t use ICMP internally, you want to know if endpoints do.

Step 3: Use AI where it’s strongest—triage and correlation

Don’t expect ML to “detect pingfs.” Expect it to:

• correlate endpoint process activity with network behavior
• group anomalies into incidents
• suppress noisy but harmless patterns
• explain why something is unusual (baseline deviation)

A practical workflow for SOC-style operations in a startup:

1. Detection flags: “ICMP payload size constant at 1,024 bytes for 6 hours.”
2. Correlation: same host accessed vector DB snapshot and then started ICMP bursts.
3. Response automation: isolate host, revoke tokens, snapshot for forensics.
4. Human decision: confirm incident severity and scope.

This is what “AI security operations” should look like: faster decisions, fewer blind spots.

Step 4: Add guardrails that reduce blast radius

Even if you detect exfiltration quickly, limit impact by design:

• segment systems that touch sensitive AI datasets
• minimize long-lived credentials for data stores
• encrypt sensitive datasets and rotate keys
• watermark or canary-token high-value files and datasets

If you’re training or fine-tuning models, also consider:

• strict access control for training corpora
• audit logs for dataset exports
• approval workflows for bulk downloads

People also ask: does ICMP storage actually work in the real world?

Answer first: it can work as a proof of concept, but it’s unreliable and slow—yet it still teaches the right security lessons.

pingfs itself warns about performance and data loss, especially on LAN targets. ICMP can be rate-limited, filtered, deprioritized, or shaped by network gear. You also need root privileges to craft raw packets, which is a meaningful barrier.

Security takeaway: attackers don’t need perfect throughput. They need enough to move secrets. A few kilobytes of API keys, model prompts, or customer identifiers is already a breach.

What to do next if you’re building AI products in 2026

ICMP-based storage is a weird corner of computing. The lesson is mainstream: your AI system’s risk isn’t only model misuse—it’s data movement you didn’t plan for.

If you’re a founder or engineering leader, pick one concrete improvement before the next sprint ends:

• instrument ICMP visibility (even basic counters)
• define an egress policy for “diagnostic” protocols
• add AI-driven anomaly detection for low-and-slow patterns
• build a response runbook that isolates endpoints fast

The startups that win the AI era won’t just build smarter models. They’ll build systems that assume creativity—by engineers and attackers alike.

When someone inevitably finds a new way to smuggle data through “harmless” traffic, will your stack notice in minutes… or in months?