Cross-user generalization (LeaveOneUser) causes 30–40% accuracy drops across all nine state-of-the-art WFP models in closed-world settings: Var-CNN falls from 75.6% to 46.8%, DF from 78.2% to 48.7%, and TikTok/NetCLR from over 80% to approximately 50% in the 10-site task. Models lose recall faster than precision, indicating overfitting to frequent behavioral patterns of seen users rather than site-invariant features.

In the boundless (boundary-free, session-unsegmented) setting with Human+Claude training, TrafficSliver reduces WFNet accuracy from 0.760 to 0.592, Cluster Anonymization (Palette) to 0.647, and Zero-delay (FRONT) to 0.706 at 2000 training samples. All three defenses assume clean session or page-load boundaries and are less effective against an attacker classifying arbitrary 5,000-packet continuous segments.

§5.7 / Table 2 evaluation

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.

§5.4 / Figure 6 detection

In open-world evaluation against 40 unseen background websites, models trained with Human+Claude traces suffer only 2–3% accuracy degradation versus 5–8% for Human-only training. This demonstrates that LLM-augmented data provides behavioral regularization that reduces false positives on background traffic, sustaining closed-world accuracy under realistic adversarial conditions.

§5.5 / Figure 7 evaluation

WFP models trained and tested on scripted browser traffic achieve near-perfect accuracy (NetCLR: 98.9%, TF: 98.3%, Var-CNN: 98.9%), but accuracy collapses to below 10% — with F1-scores near zero (TF: 0.034, Var-CNN: 0.022, ARES: 0.025) — when trained on scripted traffic and tested on real human traffic (CrossDomain). This structural mismatch means scripted datasets overestimate the real-world WFP threat by more than 80 percentage points.

§5.3 / Table 1 evaluation

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.

2026-joll-s-website-fingerprinting-nym §5.2, Table 4, Table 5 website-fingerprintml-classifiertraffic-shape

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.

2026-joll-s-website-fingerprinting-nym §5.1, Table 2 website-fingerprintml-classifier

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.

2026-joll-s-website-fingerprinting-nym §6, Tables 7–8 website-fingerprintml-classifier

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).

2026-joll-s-website-fingerprinting-nym §6.2, Table 7 website-fingerprintml-classifier

Without chunk-based padding, an XGBoost classifier identifies the target website from covert data-chunk sizes with 91% accuracy (Tranco top-100). Chunking at 2 MB reduces accuracy to 12% at a 21.3% bandwidth overhead, while 16 MB chunks reduce accuracy to near random guessing at a 480.3% overhead. Chunks as small as 64 KB already reduce accuracy to 64%, demonstrating a monotonic fingerprinting–overhead tradeoff.

2026-kamali-huma §V-C, Figure 4 website-fingerprintml-classifier

Ephemeral defenses were integrated with a WireGuard fork and deployed as Mullvad VPN's 'DAITA' (Defense Against AI-guided Traffic Analysis) opt-in feature across Android, iOS, macOS, Linux, and Windows for over one year, serving a growing number of thousands of daily users. Individual defenses are derived deterministically from seeds in 43.6 ± 4.7 ms on a commodity laptop, making per-connection unique defenses practical at VPN scale.

2026-pulls-ephemeral-network-layer-fingerprinting §1, §7 ml-classifierwebsite-fingerprint