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%  PSL-Based Adversarial Filtering at the CDN Edge: A Production Evaluation
%  arXiv:2026.05.chud.0001  ·  Chudmaxx Labs Technical Report CL-TR-2026-03
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%  Chudflare is a parody of Cloudflare. Nothing in this document reflects
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\fancyhead[L]{\footnotesize Slopwell, Chudwell, Slopkowski}
\fancyhead[R]{\footnotesize \textit{PSL-Based Adversarial Filtering at the CDN Edge}}
\fancyfoot[L]{\footnotesize arXiv:2026.05.chud.0001v2 [cs.CR] 14 May 2026}
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\title{
  \vspace{-2em}
  \textbf{PSL-Based Adversarial Filtering at the CDN Edge:\\
  A Production Evaluation Across 310 Points of Presence}
  \vspace{0.5em}
}
\author{
  Hugo Slopwell\thanks{Corresponding author: \texttt{hugo.slopwell@chudflare.com}} \quad
  Madison Chudwell \quad
  Brennan Slopkowski \\[2pt]
  \small Chudmaxx Labs, Chudflare Inc.\\
  \small Agartha Research Park, Denver, CO 80202, USA\\[6pt]
  \small \texttt{\{slopwell, chudwell, slopkowski\}@chudflare.com}
}
\date{}

\begin{document}
\twocolumn[\maketitle
  \vspace{-1em}
  \begin{minipage}{0.92\textwidth}
    \small
    \begin{center}\textbf{Abstract}\end{center}
    \noindent
    We present the design, deployment, and evaluation of an adversarial
    request-filtering system based on the Perceived Sigma Level (PSL) metric,
    a 1.0--10.0 continuous-valued classifier trained on $4.2 \times 10^9$
    labeled HTTP request samples. PSL inference is performed in-line at the
    CDN edge across 310 points of presence (PoPs) with a median added
    latency of $0.91$\,ms (p99: $4.3$\,ms). The classifier achieves
    $98.7\%$ true-positive rate at a $0.4\%$ false-positive rate against a
    held-out adversarial corpus. We further introduce \emph{Chad Fight Mode},
    a deployment posture in which all requests scoring above $5.5$ on the
    PSL axis are silently downgraded via HTTP 403, and demonstrate that the
    approach reduces ``unwanted cheekbone exposure'' by $93.4\%$ at the
    edge. We discuss the limitations of PSL as a security primitive, the
    ethical considerations of cosmetic-coded filtering, and outline a
    pipeline for federated retraining of the classifier from user-supplied
    hunch telemetry. We release neither the model weights nor the labeled
    corpus, on the grounds that we have not yet found a buyer.

    \medskip
    \noindent\textbf{Keywords:} content delivery, web security, adversarial
    classification, edge computing, looksmaxxing, parody.
  \end{minipage}
  \vspace{1.5em}]

% ──────────────────────────────────────────────────────────────────────────
\section{Introduction}

The proliferation of high-jawline traffic on the public Internet poses an
emerging challenge for content delivery providers \cite{chudwell2024edge,
slopkowski2025taxonomy}. While prior work has focused on rate limiting
\cite{rfc6585}, IP reputation \cite{spamhaus2003}, and JA3 fingerprinting
\cite{althouse2017ja3}, none of these signals adequately capture the
\emph{cosmetic posture} of an incoming request, defined here as the
joint distribution over visitor facial-harmony characteristics inferred
from User-Agent string, request cadence, and TLS ClientHello entropy.

This paper makes the following contributions:
\begin{enumerate}[leftmargin=*,nosep]
  \item We define the \textbf{Perceived Sigma Level (PSL)}, a continuous
    metric in $[1.0, 10.0]$ summarizing the cosmetic posture of an HTTP
    request (\S\ref{sec:psl}).
  \item We describe a production system, \textsc{Chud Fight Mode}, that
    performs in-line PSL inference at the edge of a global CDN with median
    added latency of under $1$\,ms (\S\ref{sec:system}).
  \item We evaluate the classifier on $1.43 \times 10^{10}$ requests
    captured in March 2026, demonstrating that it removes $93.4\%$ of
    cheekbone-bearing traffic at a $0.4\%$ false-positive rate
    (\S\ref{sec:eval}).
  \item We discuss the limitations of the approach, including its
    documented inability to reliably classify visitors who recently
    showered (\S\ref{sec:limits}).
\end{enumerate}

A note on terminology. ``Chud'' in this paper refers to the operational
class of \emph{desired} CDN visitor, characterized in our dataset by
$\textsc{psl} \in [1.0, 3.5]$, a measurable hunching angle, and at least
one Monster Ultra Zero opened in the prior $24$\,h window. We acknowledge
the term carries connotations elsewhere; in this work it is used in its
purely operational sense.

% ──────────────────────────────────────────────────────────────────────────
\section{Related Work}
\label{sec:related}

\paragraph{Edge filtering.}
The use of edge computing for security has been extensively studied
\cite{cflare2017workers, fastlyedge2019, akamai2020origin}. Most prior
systems filter on lexical or structural request features. Our work extends
this line of inquiry by introducing a cosmetic axis to the classification
boundary.

\paragraph{Facial-harmony metrics.}
The PSL scale traces its origins to anonymous fora active between
2014--2019 \cite{anon2018psl, anon2019harmony}. Rigorous benchmarks remain
scarce. Hunched et al.~\cite{hunched2022bench} attempt a normalization
to the [0,1] interval but report a Cohen's kappa below $0.2$, suggesting
fundamental inter-rater disagreement. We do not address this; our
classifier is trained against a single rater (\S\ref{sec:methods}).

\paragraph{Looksmaxxing detection.}
Recent work has examined the detection of looksmaxxing communities in
short-form video \cite{tiktok2024lmx, chudwell2025drift}. To our knowledge
this is the first work to apply analogous detectors at the CDN layer.

% ──────────────────────────────────────────────────────────────────────────
\section{The PSL Metric}
\label{sec:psl}

We define the Perceived Sigma Level of a request $r$ as a weighted
combination of three submetrics:
\begin{equation}
\textsc{psl}(r) = w_1 \cdot \phi_{\text{cb}}(r) + w_2 \cdot \phi_{\text{jl}}(r) + w_3 \cdot \phi_{\text{cad}}(r),
\end{equation}
where $\phi_{\text{cb}}(r)$ is the estimated cheekbone visibility,
$\phi_{\text{jl}}(r)$ is the estimated jawline angle from horizontal, and
$\phi_{\text{cad}}(r)$ is request cadence relative to the population
median. Weights $(w_1, w_2, w_3) = (0.42, 0.39, 0.19)$ were fit by
five-fold cross-validation against a hand-labeled corpus of $50{,}000$
requests collected by Chudwell over the spring 2025 quarter.

Crucially, none of $\phi_{\text{cb}}$, $\phi_{\text{jl}}$, or
$\phi_{\text{cad}}$ has access to any biometric data. All three are
estimated purely from HTTP-layer signals; we present this estimation
as a feature rather than a limitation.

\subsection{Calibration}
\label{sec:calibration}

We calibrate PSL such that the median chud scores approximately $2.1$ and
fewer than $1\%$ of chuds exceed $3.5$. The scale is open at the top: a
score of $10.0$ corresponds to the theoretical Pareto-optimal Gigachad,
which we have never observed in our deployment\footnote{Slopwell reports
one suspected sighting in February 2026, but the offending request was
filtered before headers could be captured.}.

% ──────────────────────────────────────────────────────────────────────────
\section{System Architecture}
\label{sec:system}

The Chud Fight Mode classifier is deployed as a hot path in the request
pipeline of all $310$ Chudflare PoPs. Inference proceeds as follows:

\begin{algorithm}
\caption{In-line PSL inference at the edge.}
\label{alg:psl}
\begin{algorithmic}[1]
\Require Incoming HTTP request $r$, threshold $\tau$.
\State $h \gets \texttt{hashClientHello}(r)$
\State $f \gets \texttt{extractFeatures}(r, h)$
\State $p \gets \texttt{PSLModel}(f)$ \Comment{$O(\text{model depth})$}
\If{$p > \tau$}
  \State \Return \texttt{Response}(403, ``you got mogged.'')
\ElsIf{$p > \tau - 1.0$}
  \State \Return \texttt{ChudChallenge}(r) \Comment{5-second hunch CAPTCHA}
\Else
  \State \Return \texttt{forward}(r) \Comment{originate to backend}
\EndIf
\end{algorithmic}
\end{algorithm}

The classifier is implemented as a 3-layer perceptron with hidden
dimension 64, exported to ONNX and compiled to WebAssembly for execution
inside our Chudders serverless runtime \cite{chudflare2026chudders}.
Inference cost is dominated by feature extraction; the model itself runs
in under $200$\,$\mu$s at p99.

\subsection{Deployment Postures}
\label{sec:postures}

Customers select among four operational postures, summarized in
Table~\ref{tab:postures}.

\begin{table}[h]
\centering
\small
\begin{tabular}{lll}
\toprule
Posture & Threshold $\tau$ & Action above $\tau$ \\
\midrule
Chuddle           & 7.5  & forward, log only \\
Chud Mode         & 5.5  & forward, log only \\
Chad Fight Mode   & 5.5  & 403 ``you got mogged.'' \\
Under Mew Mode    & 4.0  & 102 ``processing in silence'' \\
\bottomrule
\end{tabular}
\caption{Deployment postures available to Chudflare customers. Under Mew
Mode is recommended only for users actively being mogged in production.}
\label{tab:postures}
\end{table}

% ──────────────────────────────────────────────────────────────────────────
\section{Evaluation}
\label{sec:eval}

We evaluate the classifier on $1.43 \times 10^{10}$ requests captured
across all $310$ PoPs during the four-week window of March 2--29, 2026.
Of these, $9.2\%$ were labeled (post hoc) as adversarial; the remainder
served as our negative class.

\subsection{Methods}
\label{sec:methods}

Labels were assigned by Chudwell in a single sitting on March 30, 2026,
while seated, hunched, and consuming a Monster Ultra Zero. We acknowledge
this is methodologically suboptimal; see \S\ref{sec:limits} for a
discussion. All experiments were conducted on production traffic;
we did not maintain a clean test set.

\subsection{Results}

Table~\ref{tab:results} summarizes our results against three baselines:
IP reputation lookup, JA3 fingerprinting, and a generic rule-based WAF.

\begin{table}[h]
\centering
\small
\begin{tabular}{lrrr}
\toprule
Method & TPR & FPR & Med. lat. \\
\midrule
IP reputation only        & 41.2\% & 1.8\% & 0.1\,ms \\
JA3 fingerprint match     & 68.4\% & 2.3\% & 0.3\,ms \\
Rule-based WAF (94 rules) & 79.1\% & 4.6\% & 1.2\,ms \\
\textbf{PSL (ours)}       & \textbf{98.7\%} & \textbf{0.4\%} & 0.9\,ms \\
\bottomrule
\end{tabular}
\caption{True-positive rate, false-positive rate, and median added
latency across $1.43 \times 10^{10}$ March 2026 requests. PSL achieves a
$24.9$ percentage-point absolute improvement in TPR over the strongest
baseline (rule-based WAF), at lower FPR and comparable latency.}
\label{tab:results}
\end{table}

\subsection{Ablation}

To isolate the contribution of each submetric, we re-train PSL with each
of $\phi_{\text{cb}}$, $\phi_{\text{jl}}$, $\phi_{\text{cad}}$ ablated.
Removing $\phi_{\text{jl}}$ produces the largest drop in TPR ($-7.2$
points), suggesting that jawline estimation carries the bulk of the
classifier's signal. Removing $\phi_{\text{cad}}$ has negligible effect,
which we attribute to its high collinearity with $\phi_{\text{cb}}$.

\subsection{Discussion}

The PSL classifier achieves strong performance against our held-out
adversarial corpus, but several observations warrant care. First, the
true-positive rate is unusually high relative to prior work; this may
indicate label leakage during corpus construction. Second, the FPR of
$0.4\%$ corresponds to approximately $5.7 \times 10^7$ misclassified
chuds per day at peak traffic, which our customer support team
characterizes as ``a lot of angry emails.''

% ──────────────────────────────────────────────────────────────────────────
\section{Limitations and Ethical Considerations}
\label{sec:limits}

We identify the following limitations.

\paragraph{Single-rater labels.} All adversarial labels were assigned by
a single annotator over a four-hour interval. Inter-annotator agreement
was not measured, in part because we did not have a second annotator.

\paragraph{Cosmetic axis.} The premise of filtering by inferred cosmetic
posture is, strictly speaking, indefensible. We deploy it because our
customers ask for it. We do not endorse the practice and recommend its
use only as a parody.

\paragraph{Adversarial robustness.} A motivated attacker can lower their
PSL by hunching, opening Doordash, or replacing their TLS stack with that
of an unmaintained 2014-era distribution. We have observed all three in
the wild.

\paragraph{Ethical disclosure.} The authors hold equity in Chudflare,
which sells the Chad Fight Mode product evaluated in this paper. The
labeled corpus was collected without IRB review, on the grounds that
no humans were classified (only their requests). We acknowledge this
distinction is thin.

% ──────────────────────────────────────────────────────────────────────────
\section{Conclusion}

We have presented PSL, an edge-deployed adversarial classifier for HTTP
requests, and \textsc{Chad Fight Mode}, the deployment posture in which
it is most usefully embodied. The classifier achieves a $98.7\%$
true-positive rate at $0.4\%$ FPR on a production corpus of
$1.43 \times 10^{10}$ requests. Future work includes (i) extending the
classifier to support federated retraining from customer-supplied hunch
telemetry, (ii) introducing a continuous variant of Under Mew Mode in
which all responses are stochastically lowercased, and (iii) finding a
buyer for the labeled corpus.

\section*{Acknowledgments}

The authors thank the Chudflare ChudVerse 2026 program committee for
helpful early feedback, the Slop Operations team for keeping inference
latency under p99 targets, and Brennan's couch for hosting the entire
labeling session.

\paragraph{Reproducibility.} We release neither the labeled corpus nor
the trained weights. Source code for the inference path is available on
request, by sending an email and never receiving a reply. A reference
implementation of the classifier (in Chudscript) is available at
\url{https://chudflare.com/playground}.

% ──────────────────────────────────────────────────────────────────────────
\begin{thebibliography}{99}
\small
\bibitem{chudwell2024edge}
M.~Chudwell.
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\bibitem{slopkowski2025taxonomy}
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\bibitem{rfc6585}
M.~Nottingham and R.~Fielding.
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\bibitem{spamhaus2003}
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\bibitem{althouse2017ja3}
J.~Althouse, J.~Atkinson, and J.~Atkins.
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\bibitem{anon2018psl}
Anonymous.
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\bibitem{anon2019harmony}
Anonymous.
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\bibitem{hunched2022bench}
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\bibitem{tiktok2024lmx}
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\bibitem{chudwell2025drift}
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\bibitem{cflare2017workers}
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\bibitem{fastlyedge2019}
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\bibitem{akamai2020origin}
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\bibitem{chudflare2026chudders}
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\end{thebibliography}

\section*{Compliance Notice}
{\footnotesize
This document is a parody. \emph{Chudflare} is a parody of \emph{Cloudflare,
Inc.} It is not affiliated with, endorsed by, sponsored by, or in any way
related to Cloudflare, Inc. Cloudflare ships excellent infrastructure
that this paper has nothing to do with. All cited works marked
``Anonymous'' or attributed to \emph{Chudmaxx Labs}, \emph{Hugo Slopwell},
\emph{Madison Chudwell}, or \emph{Brennan Slopkowski} are fictional and
should not be entered into any reference manager that you intend to use
in a serious work. No humans were classified in the preparation of this
manuscript. For real infrastructure research, visit
\url{https://blog.cloudflare.com/research}.\par}

\end{document}
