Password-protected Castle game sessions (passwords distributed via a BridgeDB-like mechanism) prevent censors from joining instances to observe in-game state or identify participants; when a client fails to supply the correct password within a timeout, the Castle proxy falls back to an AI player, making Castle instances indistinguishable from legitimate games even to an adversary who enters the lobby.

Castle structurally avoids all three covert-channel pitfalls identified by Geddes et al.: architecture mismatch is avoided by supporting both client-server and P2P modes; channel mismatch is avoided because RTS games implement application-layer reliability over UDP (matching proxied TCP requirements, unlike VoIP), blocking selective-drop denial-of-service attacks; content mismatch is avoided because legitimate RTS traffic has high natural variance driven by map, strategy, and player count.

§6.3 defense

A single undergraduate ported Castle to two closed-source commercial RTS games (each with >8.5 million copies sold, from different studios) in under 6 hours per game using a ~500-LOC Python/AutoHotkey codebase; 17 of the Top 20 best-selling RTS games share the unit-command structure Castle requires, and 11 have community-decoded replay formats, enabling rapid adaptation to new titles.

§7, Table 1 defense

Castle's packet-size and inter-packet-time distributions (measured via Kolmogorov-Smirnov statistic) fall within the variance observed between legitimate human-game sessions when using ≤50 units/command at ~1 command/second; the best-performing classifier (Herrmann) achieved only ~60% accuracy—roughly 10% above random guessing—against multiple Castle configurations, while two other classifiers (Liberatore, Shmatikov timing) performed near chance.

§6.1, Figures 3–5 defense

Vanilla Castle achieves 42–190 bytes/second (average) and transfers a 10 KB file in 52–238 seconds depending on the game (0-A.D. / Aeons / Conquerors); game-specific exploitation of per-unit click logging in Aeons raised throughput to ~3 KB/s. These rates are sufficient for asynchronous text-based communication (tweets, email, news articles) and bootstrapping Tor bridge IP distribution.

§8, Table 2, §8.3 evaluation

Re-testing in 2025 on a Pixel 10 Pro XL running Android 16 with October 2025 security updates confirmed that blind in/on-path VPN inference attacks remain fully viable despite CVE-2019-9461, CVE-2019-14899, and CVE-2024-49734 having been formally closed. All three core attack primitives—VPN-assigned internal IP discovery, active connection inference, and TCP reset injection via sequence/acknowledgment window scanning—succeeded across OpenVPN, WireGuard, and NordLynx.

2026-tolley-architectural §5.1–5.3 traffic-shaperst-injectionactive-probing

Six widely deployed VPN and circumvention tools—OpenVPN, WireGuard/NordLynx, NordWhisper, Orbot (Tor on Android), Lantern, and Psiphon—all failed to block internal IP inference, connection-state detection, and TCP reset injection under identical adversarial conditions on fully patched Android 16. Application-layer obfuscation in Lantern and Psiphon did not prevent TCP-layer disruption; Orbot's VPN-style encapsulation of Tor traffic was bypassed via the same tunnel-level side channels.

2026-tolley-architectural §5.2, §5.4–5.5 traffic-shaperst-injectionactive-probing

The paper proposes an Internet Freedom vulnerability registry with five design principles: persistent cross-vendor tracking under shared identifiers (e.g., IF-ARCH-2025-001) as long as a risk remains reproducible; human-centered impact ratings anchored to harm potential for journalists and dissidents rather than CVSS-style exploitability scores; timestamped re-verification hooks with linked PCAPs and minimal reproduction scripts; a structured media interface to counter vendor narrative capture; and open public APIs for integration into risk dashboards so that users of tools like Orbot or Lantern can directly query their configuration's exposure to known metadata-based attacks.

2026-tolley-architectural §6.1–6.5 traffic-shaperst-injectionactive-probing

The September 2025 leak of ~600 GB from Geedge Networks and the MESA Lab (Institute of Information Engineering, Chinese Academy of Sciences) is the largest known document disclosure from the GFW vendor ecosystem. It establishes a direct lineage: MESA Lab (founded 2012 by Fang Binxing's team, annual contracted revenue >35M RMB by 2016) spun out Geedge Networks in 2018, with MESA alumni filling key engineering roles (e.g. Zheng Chao as CTO). The leak includes ~64 GB of MESA git repositories, ~35 GB of MESA internal documents, ~15 GB of Geedge internal documents, and a ~3 GB Jira export — providing direct access to source code, work logs, and internal communications behind GFW R&D.

2025-geedge-mesa-leak §4 dpiactive-probingml-classifiersni-blockingfully-encrypted-detect

Internal Geedge documents confirm active contracts to deploy GFW-derived censorship and surveillance infrastructure in Myanmar, Pakistan, Ethiopia, Kazakhstan, and at least one additional unidentified country under the Belt and Road framework, in addition to domestic deployments in Xinjiang, Jiangsu, and Fujian. The exported product (the Tiangou Secure Gateway / TSG line) is not a stripped-down export variant — leaked TSG documentation shows DPI, active-probing, ML classifiers, and granular per-region traffic control rules that mirror the domestic GFW capability set.

2025-geedge-mesa-leak §5, §6 dpiactive-probingml-classifiersni-blockingdns-poisoningfully-encrypted-detecttraffic-shape

InterSecLab frames the Geedge/TSG export program as the commoditization of national firewall capability: rather than each censor state independently developing detection infrastructure, they contract Geedge for a turnkey system incorporating the cumulative R&D of MESA Lab (>10 years, National Science and Technology Progress Award winners). This structural shift means the marginal cost for an autocratic government to acquire GFW-grade censorship is now a procurement decision, not a multi-year engineering program. The report identifies that Geedge's relationship with the MESA Lab gives customer states indirect access to ongoing academic R&D improvements, not just a static product.

2025-interseclab-internet-coup §2, §6–§7 (InterSecLab report) dpiactive-probingml-classifier