Credential stuffing is no longer a “bad week” event. For many teams, it is background noise that spikes without warning, blends into normal user behavior, and quietly drains time from higher-value security work. The hardest part is not knowing it exists, along with the awareness of phishing attacks. It is knowing it exists, and still having to decide, in milliseconds, whether a login attempt is a real customer returning or an automated run that will keep trying until something sticks.
Turning traffic diversity into a detection signal
Credential stuffing succeeds when each login attempt looks like it came from a different “someone,” even though the behavior is driven by the same automation. The defensive goal is to connect those dots without slowing down legitimate sign-ins. In this case, a proxy server becomes less of a networking detail and more of a measurement point (in this article, when we say “proxy,” we mean an edge/reverse proxy that can tidy up requests, record them, and add extra useful information before they reach your system).
Placed in front of authentication, a proxy server can standardize what your login stack sees and records. That matters because detection is usually built from small signals, such as:
- how quickly attempts arrive
- how often a username changes
- whether sessions behave consistently
- whether the request “shape” matches what real browsers tend to generate
When those signals are collected in different places, or logged inconsistently across services, the story gets blurry. A proxy layer helps keep the story readable.
On the monitoring side, proxies help you see your own defenses the way attackers probe them, without guessing. Security teams often need to validate questions like: Are challenges showing up too early for real users? Are certain regions getting blocked more than others? Are we accidentally punishing shared networks? Using controlled proxies for this kind of observation lets you test your login flow from many network vantage points, compare outcomes, and tune thresholds with evidence rather than hunches.
What the numbers say about automated login risk
Even very skilled security teams can underestimate how much of today’s internet traffic comes from bots and scripts, not humans, because a lot of it doesn’t look like a “normal attack.”
Recent reports make the picture clearer. They show:
- how big this automated traffic really is, and
- where defenders should pay the most attention, especially around account takeovers and logins that happen through APIs.
| Metric (global, 2023) | Why it matters for login defense | Reported value |
| Share of all internet traffic generated by bots | Automation is a baseline condition, not an exception | 49.6% |
| Share of all internet traffic attributed to “bad bots” | A large fraction of automation is hostile by intent | 32% |
| Share of traffic attributed to human users | The “normal” you protect is closer to half than most assume | 50.4% |
| Share of login attempts associated with account takeover | ILogin endpoints carry a meaningful slice of high-risk attempts | 11% |
| Share of account takeover attacks targeting API endpoints | Defenses must cover API login routes, not just web forms | 44% |
These figures push toward an important operational takeaway: if you treat every suspicious login as an isolated event, you will always feel behind. The volume alone guarantees alert fatigue. Instead, detection needs to be about grouping: spotting repeated structure across attempts that appear unrelated on the surface. And that’s what makes proxy-aware telemetry even more valuable. When you can reliably normalize and enrich request data at the edge, you can build stronger clusters (for example, attempts that share timing patterns, client fingerprints, and retry logic) even when the network origin constantly shifts.
Using proxy signals without locking out real users
Effective counter-measures tend to be quiet. They do not “win” by blocking everything that looks odd. They win by making automation expensive while keeping real sign-ins smooth. That requires two things: good measurement of how much of your login stream is automated, and careful use of network signals so you do not punish shared or traveling users.
A useful reference point comes from a large application security dataset: “31.2% of all application traffic processed by Cloudflare is bot traffic.” It is also worth grounding this in breach of reality. In a 2024 DBIR analysis, the use of stolen credentials is described as “the number one initial action type in breaches,” present in 24% of breaches “this year” and 31% over the past 10 years. That is the backdrop for credential stuffing: defenders are not just fighting a noisy endpoint, they are operating in an environment where compromised logins are still a primary path into systems.