The GFC identifies Tor connections via a unique TLS ClientHello cipher list sent by the Tor client. Once DPI boxes detect this fingerprint on outbound traffic, active scanning is initiated within minutes: scanners connect to the suspected bridge, attempt to build a Tor circuit, and if successful the IP:port tuple is blocked. This two-stage pipeline (fingerprint → confirm → block) allows dynamic bridge blocking without pre-enumeration.

A blocked Tor bridge becomes reachable again after approximately 12 hours if Chinese scanners are unable to reach it continuously. In the authors' experiment, one bridge (port 23941) whitelisted to their Chinese VPS via iptables was unblocked within 12 hours despite remaining actively used, while an unrestricted bridge (port 27418) stayed blocked indefinitely.

§4.2 detection

Of 2819 public Tor relays in the February 2012 consensus, only 47 (1.6%) were reachable via TCP from within China. After three days, only 1 of those 47 remained reachable. The GFC blocks relays by IP:port tuple rather than by IP to minimize collateral damage to co-hosted services.

§4.1, §4.3 detection

Over 3295 active-probing scans observed across 17 days, 51% (1680) originated from a single IP address (202.108.181.70), while 98% of the remaining 1615 addresses were unique. All scanner IPs belong to three Chinese ASes: AS4837 (65.7%), AS4134 (30.5%), and AS17622 (3.8%). TTL analysis of 85 connections shows the scanner IPs are likely spoofed by the GFC—post-scan ping TTLs differed by +1 from during-scan TTLs.

§4.5 detection

Tor DPI fingerprinting by the GFC is applied exclusively to egress traffic (from inside China to the outside world). Simulated Tor connections between domestic Chinese nodes and between external nodes connecting inward to a Chinese VPS attracted zero active scans across multiple experimental runs, indicating the detection infrastructure is positioned on the border for outbound flows only.

§4.4 detection

TLS-Attacker's Workflow Traces and Modifiable Variables mechanisms allow testers to specify arbitrary protocol flows and apply field-level modifications — including adding, removing, or overwriting individual TLS message fields — without breaking the internal TLS state machine. This makes it the standard instrument for probing how DPI systems and active-probing detectors respond to non-standard or mutated TLS handshakes.

2024-niere-tls-attacker §1 Introduction tls-fingerprintactive-probingdpi

ShadowTLS relays are detectable via three active probing techniques exploiting behavioral discrepancies from the mask sites they mimic: (1) responding to plaintext HTTP on port 443 with FIN-ACK rather than an error (only 17% of TLS servers share this behavior), (2) silently ignoring non-TLS record data post-handshake rather than sending a fatal alert (only 0.14% of 30M hosts behaved this way), and (3) silently ignoring corrupted TLS Application Data records rather than sending a bad_record_mac alert (only 0.12% of hosts silent).

2023-wang-chasing §3.2 active-probingtls-fingerprintdpi

77% of public bridges offer only vanilla Tor, which is trivially detectable via TLS certificate pattern matching. An additional 15% offer Pluggable Transports with conflicting security properties (e.g., obfs4 + obfs3 + obfs2 co-deployed on the same bridge), allowing a censor to confirm and block the bridge via the weakest PT and thereby disable all stronger PTs on the same IP — including active-probing-resistant transports like obfs4 and ScrambleSuit.

2017-matic-dissecting §V-C, Table I tls-fingerprintactive-probingdpi

Iran deployed a new Tor-blocking strategy in February 2013 that caused direct Tor user counts to collapse from over 50,000 to near zero within weeks, as recorded by Tor Project metrics.

2013-winter-towards Figure 1, §1 dpitls-fingerprintactive-probing

OONI's threat model assumes an adversary capable of country-wide traffic manipulation who may actively fingerprint and identify measurement probes. Prior measurement tools (e.g., ONI's rTurtle) used easily fingerprinted centralized DNS and HTTPS traffic, which the authors flag as a pattern to avoid. The authors acknowledge that anti-fingerprinting measures will likely reduce measurement accuracy — a trade-off unresolved at publication.

2012-filast-ooni §3 Threat Model; §10 Limits and future work active-probingdpitls-fingerprintdns-poisoning

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