Aimbot vs. aim-assist: telling them apart on a demo

A hard aimbot, a low-FOV assist, and silent aim leave three different fingerprints in tick data — and why one clean tick is never enough.

People say "aimbot" like it’s one thing. In demo data it’s at least three, and they don’t look alike. A CS2 demo records every player’s view angle (pitch and yaw), position, weapon and hit result on every tick — roughly 64 times a second — so the differences are visible if you look at the right window.

Three fingerprints

• Hard aimbot / flickbot. A large, near-instant view-angle jump onto the head followed by the shot, often with trigger timing no human produces. The snap itself is the signature: the yaw delta between two ticks is bigger and cleaner than a human flick.
• FOV-limited assist. The human aims roughly, the software finishes the last few degrees. No big snap — instead the same tiny terminal micro-correction onto the head, kill after kill, with far less variance than a wrist.
• Silent aim. The visible crosshair never lands on the target; only the bullet vector is corrected. The fingerprint is a mismatch between where the player is looking and where the damage goes — in a casual watch it just looks lucky.

Smoothing and "humanizer" modules exist to blur the first two — vendors literally advertise injected reaction delays of 150–250 ms to sit inside the human band. That’s why the durable signal isn’t the size of one correction; it’s the lack of natural variation across many of them.

What the research keys on

This is a solved-enough problem that there’s a literature. A 2012 IJCNN paper introduced behavioral features that separate aimbot users from equally accurate honest players, hitting ~93% true-positive and true-negative rates. A 2026 server-side study parsed 128-tick CS demo sequences — per-tick yaw/pitch deltas, distance to target, weapon, movement — and a stacked-LSTM model reached 88.6% accuracy at a 0.97% false-positive rate.

Valve’s own detector works the same way

VACnet, shown at GDC 2018, is a deep network trained on human Overwatch verdicts that inspects a window of pitch/yaw around each shot. Valve reported it lifted conviction rates from the 15–30% of player reports to 80–95%. The stated limitation is the honest part: VACnet is good at blatant cheating — an assist dialed down to ~60% accuracy can slip it. That’s the whole reason closet cheating is hard, and why "flagged" should mean "reviewed," not "banned."

Suspect.gg reads the same demo tick data — our aim signals include damage-to-kill timing and crosshair pre-aim error — and expresses them as a Suspect Index against a clean-player baseline, plus a percentile against the players we’ve analysed. It’s triage that points a human at the right rounds, not a verdict machine.

1. Yu et al. — A statistical aimbot detection method for online FPS games (IJCNN 2012)
2. Dhinge et al. — Server-side anti-cheat for aimbot detection (arXiv:2607.04336, 2026)
3. VACnet — "Robocalypse Now", John McDonald, GDC 2018
4. PC Gamer — how VACnet works
5. demoparser — per-tick pitch/yaw/position fields (LaihoE, GitHub)