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.

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.

§2 Background detection

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

The blog author, drawing on evaluation experience, concludes that LetsVPN's failure was not caused by IP exhaustion or ordinary node instability but by precise protocol-signature identification: once the GFW extracts a client handshake feature, it can simultaneously block all connections sharing that signature across hundreds of thousands of users.

2026-anon-letsvpn-vpn §公告里的几句大实话 dpitls-fingerprintactive-probing

FlowPaint evades all three GFW-style rule-based middlebox configurations — stateful TCP keyword reassembler (Censor A), heuristic header filter dropping flows with TCP data offset ≥ 9 (Censor B), and anti-replay validator caching (SEQ, payload_hash) pairs and injecting bidirectional RSTs on replay detection (Censor C). Geneva's duplicate-packet desynchronization strategy fails against Censor C because its malformed artifacts simultaneously satisfy the anti-replay blocking condition, while FlowPaint succeeds by recalculating checksums and injecting no extra packets.

2026-ling-one-prompt-censorship-evasion §5.2, Table 4 dpirst-injectionactive-probing

As of August 2026 in Fuzhou, Fujian, most censorship-resistant service providers are non-functional; a small number of providers using v2ray remain partially functional, while Tor Browser bridges and Snowflake are no longer usable.

2026-molotoves-wall-behind-wall Issue body dpiactive-probingtraffic-shape

The Reality protocol triggers immediate RST flood responses from Iranian ISP infrastructure at the completion of the TLS handshake, suggesting Iranian DPI has developed a fingerprint or active-probe response specifically targeting Reality's TLS handshake forwarding mechanism as of June 2026.

2026-pooribitwise-iran-advanced-dpi Issue body — Other Observations §2 dpirst-injectiontls-fingerprintactive-probing

Authoritarian regimes blocked Snowflake primarily through DPI targeting fingerprints in Pion's DTLS handshake and TLS fingerprints in complementary WebRTC protocols, not through ML-based traffic analysis — confirming that cost-effective censors consistently favor simple, deterministic methods over computationally expensive classifiers.

2026-vilalonga-obscura-enabling-ephemeral §1 dpitls-fingerprintactive-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