Proteus does not yet implement normalized or randomized error responses, and the authors explicitly flag this as a known gap: without configurable error handling, the protocol may be identifiable by an active prober who can distinguish the proxy's error behavior from that of the legitimate service being mimicked.

The GFW detects Shadowsocks by flagging apparently high-entropy connections that are not TLS or HTTP, but this detection is brittle: connections are explicitly allowed if the first 6 bytes of the first packet of a flow are all printable ASCII characters (range 0x20–0x7E). Adding a 6-byte alphanumeric preamble to the Shadowsocks message definition is sufficient to bypass this heuristic and requires only a short patch to the protocol specification file.

§3.2 detection

Marionette, the prior programmable protocol system, executes user-specified plugin code in a generic Python runtime, making proxies and clients vulnerable to a malicious or buggy protocol distributor and creating a single point of failure in distributed networks like Tor. Marionette also lacks support for multiple simultaneous protocols and version upgrades, limiting its ability to respond to changing censorship rules across heterogeneous client populations.

§4 evaluation

A complete Noise NK handshake protocol — including Curve25519 ECDH key exchange with server authentication, HMAC-based key chaining, and ChaCha20-Poly1305 AEAD-encrypted data phase — was expressible in Proteus in less than 4 hours, demonstrating that a safety-bounded DSL with built-in crypto primitives and declarative message-format definitions is sufficient to prototype complex cryptographic transport protocols rapidly.

§3.3 evaluation

Proteus supports simultaneous execution of multiple Protocol Specification Files (PSFs) on a single server, selecting the correct protocol version by running all candidates in parallel on a shared read buffer and eliminating candidates as they fail to parse client messages. This enables servers to support legacy clients while deploying new evasion protocols, and to serve clients in different censorship regimes with localized protocol variants, without requiring synchronous client/server upgrades.

§2.4 defense

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