The GFW was observed detecting Shadowsocks servers by sending follow-up active probes after an initial Shadowsocks-sized client message, including permuted replays of the client's message and random-data probes of various sizes up to and exceeding Shadowsocks' unique 50-byte data limit. This defeats shadowsocks-libev's replay cache because the GFW permutes the replayed bytes rather than resending them verbatim.

Censys scans of IPv4 HTTPS servers in June 2020 found that over 21% responded to a GET / with 400 Bad Request, 11.19% with 403 Forbidden, 8.62% with 404 Not Found, and 2.91% with 401 Unauthorized. These common error-response distributions provide a statistical baseline that HTTPT servers can match to avoid standing out to active probers.

§3.2.3 evaluation

HTTPT prototype performance is comparable to Shadowsocks: median Time-to-First-Byte was 612 ms for Shadowsocks, 844 ms for HTTPT (TLS 1.3, +1 RTT), and 1085 ms for HTTPT (TLS 1.2, +2 RTTs). Bandwidth overhead was approximately 2%: median time to fetch a 100 MB file was 24.65 s for Shadowsocks vs. 25.15 s for HTTPT.

§4.2 evaluation

HTTPT achieves replay-attack immunity by tunneling over TLS, which incorporates bidirectional nonces (client and server randoms) into key agreement so each connection uses unique cryptographic keys. Censors that replay a legitimate client's observed initial bytes are therefore unable to trigger a proxy response, unlike approaches that rely only on application-layer replay caches.

§1.1, §3.3 defense

Frolov et al. (2020) found that over 94% of Internet servers respond with data to at least one popular protocol probe, making probe-resistant proxies that remain entirely silent statistically anomalous. Censors can further fingerprint silent proxies by their unique timeout or data-limit behaviors before connection close (e.g., Lampshade closes immediately after 256 bytes of unrecognized data, or waits exactly 90 seconds before timing out).

§2 Background detection

Despite fully encrypted protocols existing since obfs2 in 2012, the first documented evidence of the GFW passively detecting them purely by randomness appeared only in 2021 — approximately a decade later — and was limited to certain foreign IP address ranges and a subsampled fraction of traffic. Meanwhile, the GFW had been discovering obfs2/obfs3 servers via active probing as early as 2013, indicating censors found active-probing-based address discovery cheaper and more reliable than passive statistical classifiers for this protocol family.

2023-fifield-comments §5 fully-encrypted-detectactive-probingrandom-payload-detect

Measured packet loss rates under GFW censorship (Feb–Apr 2017, client at Tsinghua University/CERNET): Tor with meek obfuscation suffers 4.4% average PLR; Shadowsocks (AES-256-CFB) suffers 0.77% PLR; native VPN (PPTP/L2TP) and OpenVPN both achieve ~0.21% PLR. For comparison, the same tools accessed from a US vantage point show PLR below 0.1%, confirming the excess loss is GFW-induced. The GFW's DPI and active probing techniques specifically target Tor and Shadowsocks protocol signatures.

2017-lu-accessing §4.3 dpiactive-probingrandom-payload-detect

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