Because Rebound never terminates the client–decoy connection, connection-state probes (including 0trace-style TTL-expiry probes that bypass the decoy router via an alternate route) cannot reveal any discrepancy between the observed and actual state: the connection to the decoy host is always exactly in the state a censor would expect.

He et al. found that 65% of sampled routes between public traceroute servers have some degree of AS-level asymmetry; John et al. found that asymmetry reaches 96% on Tier-1 ISP backbone links due to hot-potato routing. These figures invalidate the symmetric-route assumption underlying Telex and Cirripede and motivate a fully asymmetric design.

§I evaluation

Rebound's mole protocol generates a characteristic traffic pattern — a steady stream of long HTTP GET requests followed by 404-style error responses — that may be identifiable via traffic analysis even though the channel is TLS-encrypted; the paper acknowledges this as an unmitigated vulnerability and notes that intermingling with ordinary requests reduces observability but further lowers effective throughput.

§VIII-B detection

Rebound eliminates the stack-fingerprinting vulnerability present in Telex, Curveball, Cirripede, and TapDance by never forging packets addressed to the client; all data from the decoy router to the client travels through the real decoy host, so the TCP/IP stack fingerprint observed by a censor is always that of the genuine decoy.

§VIII-A1 defense

In an Internet measurement from a residential Verizon FiOS client 12 hops from the Rebound router (26 ms RTT), Rebound achieves 129,398 bytes/s (≈126 KB/s) for 1 MB transfers, compared to 354,676 bytes/s for Curveball and 1,174,240 bytes/s for plain HTTP — sufficient to stream 360p video but roughly 3× slower than Curveball. The unoptimised Python router implementation uses less than half a core of an Intel Xeon E5620 at 2.4 GHz at sustained full speed.

§VII, Table I 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