A malicious server operator with write access can mount a redirection attack by inserting a fake update file pointing to adversary-controlled content. If the client retrieves only k shares and Mallory controls k collaborating servers, all k update URLs match and the client proxy follows the redirect. A 1-bit non-updatable flag in the Publius URL blocks this vector by instructing clients to ignore all update files.

Publius cryptographically binds the URL to both the document content and the key shares via name_i = wrap(H(M · share_i)). Any unauthorized modification to the stored encrypted file, a share, or the URL itself causes the tamper check to fail, preventing silent content substitution by a malicious server.

§3.2, §3.3 defense

Publius's delete mechanism requires the publisher to supply H(server_domain · PW) per server rather than a bare password, preventing any single malicious server from learning the global password and deleting the document from all hosting servers. However, the paper acknowledges that an adversary who identifies the publisher can apply coercive ('rubber-hose') methods to obtain the URL and password directly from the author, bypassing all cryptographic protections.

§3.4, §5.5 defense

Publius provides source anonymity once content is published but offers no connection-based anonymity at upload time. A network-layer eavesdropper between the publisher and the servers, or a server's connection log, can reveal the publisher's IP address. The paper explicitly states that Publius must be combined with a mix-network or crowd-anonymity tool (e.g., Crowds, Onion Routing) to protect publisher identity during the upload phase.

§5.4 evaluation

Publius encrypts content under a symmetric key K, then splits K into n shares using Shamir secret sharing such that any k shares reconstruct K. Each server stores the encrypted document plus one share, so an adversary must corrupt or destroy n−k+1 servers to censor the document, and increasing n or decreasing k raises the bar proportionally.

§3.2, §5.1 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