Active probing resistance was evaluated by simultaneously querying 5 additional DNS resolvers for every domain during DNS-sly operation. DNS-sly's response change distribution falls within one standard deviation of the other resolvers, making probing attacks unable to distinguish DNS-sly servers from ordinary resolvers. TTL-based re-encoding prohibition neutralizes forced-divergence probing where an attacker sends repeated identical queries to expose responder state.

DNS-sly encodes downstream data by selecting A records from the IP address pool of CDN-hosted domains. For the top 25% of Alexa Top 500 domains, approximately one third of DNS responses contain more than 8 A records and ~15% contain 15 A records; the global IP pool has a median of ~2,000 IPs per domain (maximum ~16,000), enabling b = floor(log2(s!/(s-c)!)) bits per response.

§2.2 defense

DNS-sly achieves downstream throughput of up to 600 bytes of hidden data per web page click, with a median of ~100 bytes/click using a global IP map and ~75 bytes/click using a local IP map (a 25% difference despite vastly different IP set sizes). A 4 KB file transfer completes in 30 clicks with the global profile map and 64 clicks with the local map.

§4.2 evaluation

DNS-sly requires out-of-band distribution of a 2.3 MB compressed bootstrap package (user profile map) before covert communication begins. The authors explicitly reject automated in-band bootstrapping to preserve deniability, accepting a hard scalability constraint as the cost; the particular censored environment tested did not interfere with DNS traffic at all, enabling successful censored-site retrieval at the same throughput rates as uncensored tests.

§3.2, §4.2 defense

DNS-sly achieves statistical deniability by profiling each user's organic DNS behavior — recording accessed domains, semantic topics, and resolver-specific IP addresses — and constructing upstream requests that semantically overlap with that profile. Upstream communication is indistinguishable from normal DNS traffic in volume, frequency, and semantics; all DNS headers are fully legitimate with no unusual record types.

§3.1, §4.1 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