Incoming traffic is the dominant website fingerprinting leakage vector in Nym because the network requester does not apply Poisson-rate cover traffic by default. The Features In feature set achieves 72.1% random-forest accuracy on Nym Testnet — exceeding Tor's 70.2% — while outgoing Features Out leaks only 43.9% (vs. 65.0% for Tor). The combined Features In/Out (70.0%) provides no additional information beyond Features In alone, confirming the inbound channel is the bottleneck. Enabling Poisson-rate cover traffic on the network requester side (lambda_QR = 10-20 ms) lowers the Tik-Tok F1 score from 0.87 to 0.75-0.64 but raises latency overhead to 7.32-12.66x (Table 4, configs 10-11).

Increasing Nym's per-hop mixing delay (λM) counterintuitively worsens WF resistance: without cover traffic, raising λM to 100 ms raises Tik-Tok F1 from 0.91 (no mixing) to 0.98. Larger delays create a visible temporal gap between outgoing request bursts and incoming response bursts, making the Transposition feature set leak 95.5% classifier accuracy versus 88.5% with no delay at all.

§5.2, Table 4, Table 5 evaluation

In controlled Testnet conditions, the Tik-Tok deep-learning WF attack achieves F1=0.87 (closed-world) and F1=0.83 (open-world) against Nym's default configuration, compared to F1=0.94/0.91 on Tor. On the live Mainnet, real-world network jitter reduces attack effectiveness to F1=0.69/0.71, but this comes at 11.93× latency overhead and 8.97× bandwidth overhead — a side-effect of instability rather than designed protection. The Mainnet drop is attributable to geographic node spread and roughly 5% packet retransmissions rather than to designed obfuscation.

§5.1, Table 2 evaluation

Routing web traffic through Nym's constant-rate traffic stream (POISS-OFF) reduces Tik-Tok F1 to 0.06, with all feature-set leakage at ≤9.5% accuracy (Table 8), at the cost of latency overhead 7.32 and bandwidth overhead 8.06 (Table 7). Nym's 2,413-byte packet size means each cover packet incurs 4.7× the overhead of a Tor cover cell, making constant-rate defenses significantly more expensive in this architecture. This matches near-ideal Tamaraw protection on Tor, against WTF4Nym's F1 = 0.39 at 3.78x latency and 4.22x bandwidth.

§6, Tables 7–8 defense

WTF4Nym, a burst-aware cover-traffic defense adapted to Nym's Poisson-rate architecture, reduces the Tik-Tok attack F1 score from 0.87 to 0.39 at moderate overhead (latency overhead 3.78×, bandwidth overhead 4.22×). This substantially outperforms directly applying WTF-PAD to Nym (F1=0.87), applying FRONT to Nym (F1=0.63), and the F1=0.65 achieved by FRONT on Tor at comparable overhead, by learning per-network burst distributions and injecting fake bursts that match those statistics. FRONT on Tor reaches F1 = 0.65 at 2.79x latency and 1.84x bandwidth, and under WTF4Nym the Time and N-gram feature leakage fall to 26.6% and 29.0% (Table 8).

§6.2, Table 7 defense

IP-based fingerprints are operationally stable: approximately 60% of domains exhibit no IP changes across the three-month measurement period for both IPv4 and IPv6. Among churning domains, per-domain churn behavior is comparable across protocols—about 28% of churning IPv4 domains rotate within one hour on average vs 35% for IPv6. Fingerprint databases of ≈100 KB per site (50–60 GB for 520K sites) can be maintained with only periodic DNS re-resolution at negligible overhead for adversaries already collecting NetFlow data.

2026-ahmad-more-space-less §6.4, §5.3.2 website-fingerprinttraffic-shape

IP-based website fingerprinting achieves 93–94% top-1 accuracy on dual-stack incomplete websites (those with any IPv4-only third-party dependencies) using both IPv4 and IPv6 fingerprints across 228K sites. For dual-stack complete sites (all resources reachable over IPv6), accuracy drops to 56% over IPv4 and 45% over IPv6. The discriminating signal comes almost entirely from secondary resource IPs: using only the primary domain's IP yields 5–8% accuracy regardless of protocol.

2026-ahmad-more-space-less §6.1, Table 1 website-fingerprinttraffic-shape

With infinite training time, Laserbeak achieves 93.5%, 95.9%, and 95.9% accuracy against ephemeral padding, FRONT, and Interspace respectively, compared to 96.5% undefended — confirming that padding-only defenses provide no meaningful protection against a sufficiently trained deep-learning WF adversary. Only ephemeral blocking defenses retain measurable protection, reducing Laserbeak to 71.8% accuracy under infinite training versus 96.5% undefended.

2026-pulls-ephemeral-network-layer-fingerprinting §5.3, Table 2 ml-classifierwebsite-fingerprinttraffic-shape

Joint multi-task training with a combined loss L_joint = L_site + λ·L_pers shows that increasing λ from 0 to 2 raises mixed-site persona accuracy from approximately 45% to approximately 80% while website accuracy declines only from approximately 90% to approximately 75%, demonstrating a wide regime where an attacker can gain strong persona inference at modest cost to existing WFP capability.

2026-song-personafingerprint-measuring-persona §4.4, §5.7.2, Figure 5 website-fingerprintml-classifiertraffic-shape

Using only 1,000-packet windows of signed packet lengths and inter-arrival times (no payload, no URLs, no cookies), a passive adversary achieves approximately 84% accuracy at inferring behavioral persona in a mixed-site open-world setting spanning 10 modern websites and 15 canonical personas plus an open-world class. Per-site persona macro-F1 typically ranges from about 0.78 to 0.91 across representative platforms including Bilibili, eBay, Yahoo, Zhihu, and LinkedIn.

2026-song-personafingerprint-measuring-persona §5.3, §5.5, Abstract website-fingerprinttraffic-shapeml-classifier

Training WFP classifiers with LLM multi-agent simulated traces boosts cross-user accuracy to the 80% range (TikTok: 50.3%→84.9%, NetCLR: 53.6%→77.3%) compared to under 15% for models trained on scripted-only traffic. ClaudeOnly training outperforms ScriptedOnly baselines by approximately 3× on held-out human traces, using only 20% of the data volume, at a cost of roughly $10/GB versus $35/GB for human collection.

2026-song-redefining-website-fingerprinting §5.4 / Figure 6 website-fingerprintml-classifiertraffic-shape