A censor can identify Slitheen relay connections by observing that all packets in a suspected overt flow arrive in strict order while flows from the same source naturally exhibit out-of-order delivery: the relay station's traffic-server component reorders TCP segments to enable TLS record decryption, creating a statistically anomalous per-connection ordering pattern. The reordering buffer also increases per-packet round-trip times, providing a secondary timing signal.

Slitheen++ achieves a median covert site loading time of 7 seconds in the naive setup, rising to 8 seconds with crawling and 13 seconds with a 1-second thinking-time (TT) delay. The Baseline-to-Covert factor ranges from 3.7–8.5 without TT and from 7.6–21.4 when crawling and 1-second TT are combined, reflecting the fundamental tradeoff between stealth overt behavior and covert throughput.

§5 (Figure 1, Figure 2) evaluation

Slitheen++ embeds covert upstream data by applying HTTP/2-like header field compression to overt HTTP requests, using the recovered space for covert data placement. This ensures that neither timing information nor observable changes to packet sizes or delays can reveal decoy routing use to an omni-scientist passive censor. GZIP compression was explicitly avoided to prevent the CRIME side-channel attack.

§4 defense

Slitheen++'s relay station introduces minimal overt forwarding overhead: 95% of setups saw downstream per-packet delays between 1 ms and a maximum of 4 ms, with on average only 0.0029% of downstream packets affected (peak 0.006% in any single scenario). Upstream delays were similarly low except for a single outlier near 60 ms caused by thread contention during crawling-induced relay load spikes.

§5 (Table 1) evaluation

The original Slitheen appended covert upstream data directly to overt HTTP requests, significantly changing upstream traffic patterns and enabling censor identification even when traffic is encrypted. This upstream traffic analysis vulnerability—absent from Slitheen's original threat model—is the primary weakness Slitheen++ addresses.

§1, §4 detection

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

The server-side variant of the blind VPN inference attack—where an in/on-path adversary exploits predictable NAT assignment and tunnel routing semantics to inject spoofed packets indistinguishable from legitimate encrypted traffic—has remained unacknowledged and unmitigated across all tested platforms since its concurrent disclosure in 2019. Unlike the client-side variant, which received partial fixes from Google (CVE-2019-9461, CVE-2024-49734) and Apple (iOS 17.2.1), no vendor has proposed a viable remediation or claimed ownership of the server-side attack surface.

2026-tolley-architectural §2.1, §3.1, §3.4 traffic-shaperst-injectionmiddlebox-interference

Per-flow RTTdiff detection rates are only ~20% because the majority of proxy flows connect to CDN-cached content (Cloudflare, Google, Fastly) that sits within 5ms of the proxy, suppressing the discrepancy. However, aggregating across flows per website visit yields detection rates exceeding 70%—and from the abstract, approximately 80% of top-5K domains generate at least one detectable flow—with half of those detections made within the first 60 packets. This means an adversary can reliably expose client and proxy IPs after just a few website visits.

2025-xue-discriminative §VI-C-2, Table II traffic-shapeflow-correlation

IMAP/SSL traffic on port 993 constitutes less than 1% of total ISP traffic but accounts for nearly one third of all false positives in the RTTdiff exploit, because IMAP's non-RESTful multi-connection pattern violates the request-response correlation assumption. The overall per-flow FPR is bounded at 0.6–0.7% (on par with GFW's estimated FPR against fully-encrypted proxies), but implementing a pre-filter to whitelist IMAP traffic reduces the FPR by approximately one third, making the fingerprint substantially more precise.

2025-xue-discriminative §VI-C-3, Table III traffic-shapeflow-correlation