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).

Combining all three active probing attacks in an Internet-wide scan of 30 million HTTPS servers identified approximately 15,000 hosts (0.05%) behaving like ShadowTLS relays; of these only 6,000 presented TLS certificates for Alexa Top 1000 domains. The scan successfully discovered all four researcher-operated ShadowTLS relays planted as ground truth.

§3.2, Table 2 evaluation

The root vulnerability in ShadowTLS is that the relay cannot authenticate post-handshake data from the real mask site, causing it to silently absorb censor probes. The fix — deployed in ShadowTLS v0.2.3 — has the client re-derive the Application Data encryption key from the server random and the client-relay shared secret; unrecognized records (lacking the shared secret) are transparently forwarded to the mask site, so all censor-visible responses come from the real mask server.

§4 defense

ShadowTLS is structurally limited to TLS 1.2 because in TLS 1.3 the Finished message is sent as encrypted Application Data (record type 0x17), preventing the relay from detecting handshake completion without decrypting the session. This forces ShadowTLS to advertise TLS 1.2, which is an increasingly anomalous fingerprint as TLS 1.3 adoption grows.

§2.2 detection

ShadowTLS's TLS ClientHello fingerprint (JA3 hash ebaa863800590426) was not observed in the TLSFingerprint.io dataset collected from a university network tap, making the client fingerprint unique to the tool and trivially blockable by censors maintaining a TLS fingerprint blocklist.

§3.2 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

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 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.

2012-winter-great §2, §4.1 tls-fingerprintactive-probingdpi

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