Simulated Attack: How a 33% consensus risk puts Sui one incident away from a network halt

TL;DR

In my external posture analysis of Sui validator infrastructure, I found ≈39.6% of voting power was externally vulnerable - above the 33% consensus halt threshold by ~6.6 percentage points (equivalent to 621 voting power in our dataset).

This simulated attack models how an attacker could chain public signals and operational misconfigurations to disable enough validators to cross that threshold.

The result is a resilience warning: the network was, at scan time, within striking distance of a service-impacting halt.

Full details here:

https://github.com/pgdn-oss/sui-network-report-250819/blob/main/simulated_attack.md

Ethics & scope

This was a non-exploitative simulation using only publicly-observable data. I did not access private systems, exfiltrate data, or run exploits. I have redacted IPs, hostnames, step-by-step exploit primitives and any reproduction commands that would enable misuse. Operators who find themselves in the report and need confidential help: open an issue on the repo or contact me privately via the repo's issue tracker. I follow coordinated disclosure best practices.

Why the numbers matter

• 33% halt threshold: Sui's consensus can be materially impacted if ≥33% of voting power goes offline or is disabled.
• Observed exposure (~39.6%): My scans found roughly 39.6% of voting power had externally-observable vulnerabilities or misconfigurations that an attacker could plausibly target.
• Delta: That is ~6.6 percentage points above the halt threshold — 621 voting power in our dataset. In plain terms: the network was within a single coordinated incident of crossing a critical resilience boundary.

This isn't an abstract metric — it maps operational exposure to consensus risk. When you combine exposed validator surfaces at scale, you stop abstracting "nodes" and start measuring real systemic fragility.

What the simulated attack actually shows

The simulated attack is a modelling exercise — it demonstrates attacker decision-making rather than executing an exploit. Steps (sanitised):

1. Reconnaissance: collect public signals (metrics, HTTP banners, management port responses).
2. Enrichment: parse metric labels and banners to infer roles and topology (which nodes are validators, leaders, etc.).
3. Prioritisation: rank targets by attacker attractiveness — validators with exposed metrics + reachable management surfaces are high-value.
4. Confirmatory enumeration: light, non-destructive probes to validate co-residency and service fingerprints.
5. Attack-path modelling: chain the signals into a plausible escalation path that, if realized, could disable selected validators (e.g., by misconfigurations, exposed management APIs, or operational errors), potentially pushing cumulative offline voting power above the halt threshold.

Key point: the simulation ties what is observable from the outside to what an attacker would prioritise. It’s the mapping from telemetry -> decisions -> systemic outcome.

Concrete quantitative findings (sanitised)

• Total endpoints analysed: ~122 Sui-related endpoints.
• Voting-power exposure observed: ≈39.6% of total voting power showed externally-observable vulnerabilities by our conservative scanner and confidence policy.
• Consensus threshold context: The 33% threshold is a critical operational boundary for consensus liveness; the observed exposure exceeded this by ~6.6 percentage points (621 voting power in dataset terms).
• Common signal types driving exposure: public metrics exposing role labels, management APIs reachable on common ports (container management fingerprints appeared repeatedly), and default HTTP/admin pages leaking product/type info.

(Exact tables, heatmaps, and per-validator rows are in the full report; I have redacted host-level identifiers from this article.)

What this means for Sui (and similar networks)

• Resilience is operational, not just cryptographic. Excellent protocol design doesn't prevent nodes from being misconfigured or deployed insecurely. If enough validators share similar deployment mistakes, the protocol's liveness assumptions are at risk.
• Decentralisation ≠ diversity of security posture. A network of validators operated similarly — including shared misconfigurations, concentrates systemic risk.
• The practical impact of a coordinated incident: crossing the 33% threshold could cause temporary halts, delays in finality, staking reward disruption, and a loss of confidence among users and delegators. Even short outages can have outsized reputational costs for a young ecosystem.

What operators and the ecosystem should do now

For validators (immediate):

1. Inventory externally-exposed services for your validator and associated infra. If you can't list them, you're blind.
2. Close management APIs to the public (bind to localhost or private networks; require VPN/mTLS jump hosts).
3. Protect metrics — use private scraping or authenticated gateways; remove internal hostnames and role labels from public metrics.
4. Silence banners & versions that leak product/version info.
5. Run external posture checks against your own endpoints and triage findings immediately.

For the Sui ecosystem (coordination & incentives):

1. Require external-risk audits as part of validator onboarding. Make passing an external posture check a first-class requirement.
2. Incentivise ops maturity — link staking, eligibility, or onboarding checks to evidence of secure deployment.
3. Support operator tooling — provide vetted scanner tooling and an official remediation playbook.
4. Share anonymised telemetry so the community can track progress and systemic risk without exposing individual operators.

Limitations & responsible framing

• The 39.6% figure is based on conservative heuristics and an externally-observable posture scan — operator verification can reduce false positives. Some "exposed" signals are port-only observations or default pages that do not necessarily imply 'compromiseability'.
• This is not a claim that the network was attacked, only that the modeled conditions could — with additional operational error or a coordinated attack — cross the consensus threshold.
• My goal is operational improvement: to turn a surprising statistic into urgent, practical action.

Reproducibility & where to find the data

Full dataset, scripts, heatmaps and appendices are in the report: https://github.com/pgdn-oss/sui-network-report-250819

If you operate validators or infrastructure that appear in the report and want private assistance, please open an issue on the repo and I will respond via coordinated disclosure.

And there's a cool discord bot called PGDN Sentinel that you can use too.

https://pgdn.ai/pgdn-sentinel-discord

Final word

This isn't an alarmist headline. It's a measured warning based on data: if multiple operators expose similar surfaces, consensus-level fragility is not hypothetical — it's quantifiable and fixable. The immediate wins (close management APIs, protect metrics, automate posture checks) dramatically reduce the chance of a coordinated incident.

(I’m working on something new here — automating external risk discovery at scale. I’ll share details soon.)