A warden can fingerprint the specific covert destination a Telex user is visiting by comparing observed latency distributions against a pre-built database of covert-destination latencies. With an intelligently filtered database of only 10 distributions (K-S inter-entry threshold 0.8), the AUC is 0.868, and with approximately 12 collected samples the false positive rate drops below 10%. Larger databases (size 50) degrade to AUC 0.537 due to distribution similarity, but threshold-based filtering restores substantial discriminative power.

A routing-capable warden can enumerate over 90% of decoy-router-deploying ASes for deployments as large as 4,000 ASes using an intersection-based discovery attack: the warden probes many paths, accumulates a set of 'clean' ASes, and prunes candidate paths until a single 'tainted' AS remains. All evaluated wardens (China, Syria, Iran, Australia, France, Venezuela) achieved roughly equal detection success across all deployment sizes.

§4.1 detection

Containment analysis shows that surrounding China with a 'ring' of decoy routers at AS-hop depth 1 requires covering 161 ASes; depth-2 expands by a factor of more than 23, becoming untenable, while depth-3 is slightly smaller but leaves the majority of the Internet reachable via clean paths. Cutting China off from at least half the Internet would require all 96 of the world's largest ISPs to deploy decoy routers at all exit points simultaneously.

§6, Table 2 evaluation

Under the Cirripede 'random ASes' deployment scenario — where 0.4%–1.0% of ASes deploy decoy routers — routing-capable wardens need only disconnect themselves from 0.85%–3.04% of the Internet to obtain clean (decoy-free) paths to all remaining destinations. Even at 10% Internet-wide deployment, wardens are cut off from only 7%–9% of non-participating ASes on average.

§4.2, Figure 2 detection

A passive timing attack using the Kolmogorov-Smirnov test on connection latency distributions reliably distinguishes Telex users from honest hosts: K-S scores against the overt destination max out at 0.26, while scores against covert destinations (even those within 10 ms of the Telex station) range from 0.3–1.0 with a median of 0.7 for nearby servers and 1.0 for Alexa top-100 sites. The attack is effective even for clients 50–250 ms from the Telex station, with no K-S score below 0.26 observed across 40 PlanetLab hosts.

§5.1–5.2, Figures 5–8 detection

Tor encrypts payload but does not obscure traffic volume, leaving a residual publisher-vs-reader asymmetry: a user publishing a home video generates a markedly different upload/download ratio than one reading news. The paper also notes that website fingerprinting attacks — where the adversary pre-downloads hundreds of popular sites and matches traffic patterns to a Tor client's stream — remain possible even through bridge circuits, and are exacerbated by Tor's varying supported protocols (web vs. IM produce different timing signatures).

2006-dingledine-design §8.2 traffic-shapewebsite-fingerprintflow-correlation

A three-stage detection pipeline exploiting the "dual-role" behavioral fingerprint of single-IP circumvention relays achieved 23.2% recall (96/414 ground-truth relays) with a 0.18% false-positive rate against 97,651 benign TLS servers, for an overall accuracy of 99.5%. The ground-truth set covered OpenVPN, WireGuard, and SOCKS relays identified in a 17 TB single-day backbone trace (WIDE Project, April 9, 2025).

2026-almutairi-server §3.4, Table 1 traffic-shapedpiflow-correlation

Multi-censor simulations show that single-censor-optimized distribution strategies perform suboptimally in realistic multi-region deployments. When two networks have different censor strategies (e.g., one optimal, one zig-zag), the distributor cannot detect that a proxy is blocked until all censors have blocked it; this leaves clients without reachable proxies despite the proxy appearing "available" from the distributor's view. The authors conclude that "single-censor evaluation does not accurately predict more realistic deployment performance." A zig-zag censor in one region with 0.25 weight caused 44.4% collateral damage while reducing proxy lifetime to a median of 4 steps.

2026-fares-game §6, Table 4 traffic-shapeflow-correlation

The zig-zag traffic analysis attack (confirmed supported in Geedge TSG leak) rapidly enumerates all static proxy pools. With ζ_watch ∈ {4, 6} steps and a best-quality classifier (ρ_TP=0.99, ρ_FP=0.001), almost total proxy enumeration and user blockage occurs well before step 300. Even ζ_watch=2 leaves ~50% of users blocked. Collateral damage is high across all settings when ζ_watch ≥ 4: eventually ~50% of innocent servers are also blocked. However, Snowflake-style ephemeral proxies resist zig-zag effectively: reachability remains above 95% after 360 steps because churn prevents the censor from expanding its known proxy set beyond agents' direct assignments.

2026-fares-game §5.1, Fig 3, Fig 4 traffic-shapeflow-correlation

Censorship classifiers and traffic analysis attacks consistently exploit the initial seconds of a proxy connection, where packet-size, inter-arrival-time, and burst features are maximally discriminative. Cited work demonstrates that website fingerprinting classifiers trained solely on the first few seconds of Tor traffic achieve high accuracy, and real-world GFW detection of fully-encrypted protocols also targets early-connection bytes.

2025-pereira-extended §1 traffic-shapewebsite-fingerprintml-classifierdpifully-encrypted-detect

The framework's GAN-based schedule generator trains on short session windows (e.g., the first 10 seconds) of real browsing traffic from the Tranco Top 1000 sites, learning joint distributions of packet sizes, inter-arrival times, and burst patterns to produce realistic synthetic schedules. This repurposes GAN architectures previously used for traffic analysis (e.g., GANDaLF) as a defense-side cover-traffic generator.

2025-pereira-extended §2.2 traffic-shapewebsite-fingerprintml-classifier