Shadowsocks 'stream cipher' methods lacked integrity protection on ciphertexts, enabling a decryption oracle: an attacker who can guess as few as 4 bytes of plaintext prefix (5 bytes without controlling a /24) can replay a recorded session with a modified 7-byte target header, causing the server to send the decryption of the entire recorded stream to an attacker-controlled host. This provides an efficient active test for identifying Shadowsocks servers; once identified, a censor can block by IP address.

Censors optimize for utility under asymmetric misclassification costs rather than raw accuracy: false positives (blocking legitimate traffic) carry economic and political costs that make censors conservative about deploying classifiers with high false-positive rates. Multi-flow stateful classifiers — such as the obfs4 Elligator probabilistic distinguisher, which requires correlating observations across multiple connections — are operationally more expensive than single-packet or connection-initiation classifiers, which the author suggests explains why probabilistic multi-flow distinguishers have not been exploited in practice even when theoretically available.

§5 evaluation

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.

§5 evaluation

Three independent implementation flaws in obfs4proxy's Elligator encoding made obfs4 public-key representatives passively distinguishable from uniform random bytes: (1) non-canonical square roots allowed a square-then-root test matching 100% of obfs4 outputs but only ~50% of random strings; (2) bit 255 was always zero; (3) only large prime-order subgroup points were encoded. A classifier exploiting these achieves 100% sensitivity (obfs4 never falsely marked as random) at less-than-100% specificity. All three were fixed in obfs4proxy-0.0.12 (December 2021) and 0.0.14 (September 2022).

§3 detection

VMess's encrypted command block used a non-keyed hash over variable-length fields in a MAC-then-encrypt construction where the receiver cannot locate the hash without first parsing the protected data, enabling an active distinguishing attack: by replaying an authentic request 16 times with the padding-length field P set to 0000–1111, an attacker observes that a VMess server reads exactly P+N+4 bytes before disconnecting, with max and min byte counts differing by exactly 15 with every intermediate value present. V2Ray mitigated this in v4.23.4 by disconnecting after a timeout rather than after receiving a full command block.

§4 detection

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