Salmon's defense against the active zig-zag attack — where a censor blocks a known server to force users onto new ones and watches for correlated reassignments — requires both per-user authentication (unique login credentials per server so unauthorized probes receive a plausible HTTPS page) and traffic camouflage. Without authentication, the server must respond as a functioning proxy to any connection, fully exposing itself to the censor; without camouflage, even a rejected connection may reveal the server's nature.

Salmon simulations show that a censor with agents comprising 1% of 10,000 users can block at most 4A servers (one block per agent per full group) against a system with 1,000–2,000 servers; server groups with a hard cap of M=10 users that fill entirely with legitimate users before any agent joins become permanently invincible to server discovery. The censor's optimal strategy is to ensure each agent is always alone in its group at the time of joining, which requires knowing the user arrival rate — information Salmon withholds by not publishing user statistics.

§5.1 / §5 evaluation

Without recommendation-tree grouping logic, a censor starting agents at trust level 6 who each recommend 1–2 additional agents (requiring 4–5 months of waiting) can cut off over 95% of users even at agent percentages in the 15–30% range, as shown in Figure 6. With recommendation-tree grouping enforced, the same attack at equivalent agent fractions produces dramatically lower service disruption because agents cluster among themselves rather than spreading across innocent user groups.

§5.3 / Figure 6 detection

Salmon's trust-level mechanism (7 discrete levels; promotion from level n to n+1 requires 2^(n+1) days; banning triggered when suspicion exceeds T=1/3) reduces the fraction of users cut off by an attacking censor by more than 3× relative to rBridge under the same agent-percentage conditions. Simulations with 10,000 users (1–10% censor agents) and 1,000–2,000 servers show that trust levels keep high-seniority innocent users isolated from newer users where agents concentrate.

§5.2 / §3.6 defense

A single harvesting script running for 9 days on one free Amazon EC2 instance verified 3,101 working VPN Gate servers by testing 44,039 IP addresses, demonstrating that VPN Gate's collective defense mechanism — which relies on detecting automated scanning patterns — can be fully bypassed by routing successive queries through previously verified VPN servers. This result implies that a censor could, with no collateral damage, essentially completely shut down VPN Gate by blocking all verified servers.

§4.1 evaluation

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