How Do Anti-Cheats Detect ESP and Wallhacks?

Anti-cheats detect ESP and wallhacks primarily through three techniques: signature scanning for known rendering hooks and Direct3D/Vulkan overlays, behavioral analysis correlating player movement and pre-aim with information they "shouldn't have," and server-side fog-of-war culling where the server only sends visible-player data to each client. The 2026 trend is heavy server-side culling — Fortnite, Valorant, and Apex now send only client-visible player coordinates, making memory-read ESP less informative.

What is ESP in Video Games?

ESP (Extra Sensory Perception) is a category of video-game cheat that overlays information about enemies, items, and game state onto the player's screen that the game would not normally reveal. Typical ESP features include 2D bounding boxes around enemy players, skeleton bones, health bars, distance text, weapon names, loot rarity highlights, and line-of-sight indicators. ESP is rendered either by hooking the game's render pipeline or by drawing through an external overlay.

A wallhack is a category of video-game cheat that allows the player to see enemies, items, or other game-state elements through solid geometry such as walls, terrain, and objects. Wallhacks are implemented either as visibility-checked ESP that highlights enemies even when occluded, or by modifying the game's wall material shaders to render walls transparent. Wallhacks are one of the oldest cheat types, dating to Quake 2 chams in the late 1990s.

How Do Anti-Cheats Detect Aimbots?

Anti-cheats detect aimbots through three layered techniques: signature scanning (matching cheat binaries and known code patterns in memory), input/behavioral analysis (statistically anomalous mouse movement and reaction time distributions), and server-side validation (replay re-simulation comparing the player's reported view angles against what the demo file shows). Aimbot detection has shifted heavily toward behavioral ML in 2025-2026 — Anybrain, VACnet, Zakynthos, and Riot's ML pipeline are the new battleground.

How Does Behavioral ML Detect Cheaters?

Behavioral ML detects cheaters by training machine learning models on labeled gameplay data — confirmed cheaters versus legitimate players — and flagging sessions whose input statistics, gameplay patterns, or outcomes are anomalous. Inputs include mouse-movement curves, reaction-time histograms, recoil compensation, view-angle smoothness, kill rates, and headshot percentages. Detection happens server-side, takes hours to days for confident calls, and has been the dominant detection layer for aimbots in 2025-2026 — Anybrain, VACnet, Zakynthos, Defense Matrix.

How Anti-Cheats Detect ESP and Wallhacks — 2026

ESP (Extra-Sensory Perception, the cheat-community name for wallhack/visualization overlays) is the second-largest cheat category after aimbots. Detection has historically been harder than aimbot detection because ESP doesn''t modify input — it adds information to the player''s view, which is much harder to spot from behavior alone. The detection landscape has shifted significantly with server-side fog-of-war culling.

Layer 1 — Signature scanning for rendering hooks

ESP overlays render onto the game''s D3D11/D3D12/Vulkan swap chain. Doing so requires hooking the present routine, the device context, or the rendering pipeline. AC signature scanning targets these hook patterns: known IAT hooks in d3d11.dll/dxgi.dll, manually-mapped renderers with characteristic shader signatures, common open-source ESP libraries (ImGui patterns, kiero hook patterns), and overlay-injection trampolines. This catches the bulk of low-effort and public-cheat ESP.

The fast-evolution response from cheat developers: render the ESP outside the game''s D3D context entirely — onto a separate borderless window, an external display, or a DMA-driven overlay rendered on a secondary PC. This moves the rendering surface out of AC visibility but introduces other costs (latency, alignment, hardware complexity).

Layer 2 — Behavioral correlation

Server-side ML asks: "did this player''s movement pattern make sense given what they should have known?" Pre-aiming around corners at the exact location an enemy is about to appear, peeking from cover with millisecond timing matching an unseen enemy''s position, rotating to engage threats that were never on screen — these patterns are statistically detectable when accumulated over many sessions. VACnet does this for CS2 demos, Valorant does it server-side, Fortnite''s replay tools support it. Detection is conclusive when the analyst can answer "the player aimed at this position before they could have known the enemy was there."

Layer 3 — Server-side fog of war

The most consequential 2024-2026 development: server-side player culling. The server, knowing all player positions, only sends position updates for players who should be visible to each client based on line of sight, occlusion, and game-state visibility. If the server hasn''t sent player B''s position to player A''s client, player A''s memory doesn''t contain player B''s position — and memory-read ESP simply can''t show B on the wallhack. This is the technical solution that makes information-cheating much harder than it was in 2020.

Fortnite shipped aggressive server-side culling in 2022-2023. Valorant has done this from launch. Apex Legends improved its culling in 2024. PUBG''s culling remains looser than Valorant''s (a known trade-off given PUBG''s map sizes). The general direction is more aggressive culling, finer-grained visibility computation, and increasing willingness to accept some pop-in for security gains. See Apex Legends Cheats Guide and Fortnite Cheats Guide for the per-game state.

Where ESP still works

Games with permissive culling (CS2 to some extent, some battle-royale modes, larger-map shooters where culling is computationally expensive), games with no server-side culling at all (older or smaller competitive games), and information that''s necessarily client-resident (your own teammates'' positions, dropped loot, certain map objects). ESP for non-occluded but distant enemies is a different problem than ESP through walls — the former is easier to maintain because the data is legitimately available to the client.

What ESP detection cannot do alone

A perfectly-implemented ESP that uses only client-resident information (no behavioral aim correlation, no rendering hooks visible to AC) is genuinely hard to detect purely from the cheat itself. The detection vector becomes the player''s behavior — pre-aim, movement patterns, decision-making consistent with information they shouldn''t have. Information-only cheats are statistically harder to catch via behavioral ML alone, which is why ESP-only setups have historically had longer cheat lifecycles than aimbot setups.

Practical impact

For RawCheats users, the ESP-relevant guidance is: don''t pre-aim, don''t peek with information-perfect timing, don''t make rotations toward enemies you "shouldn''t know are there." Behavioral discipline beats most ESP-detection ML. The Raw products ship with tournament-tier ESP options (filtered by distance, by line-of-sight estimate, by relevance) that emphasize subtlety over flash. See per-game cluster posts for tuning.

Forward look

The ESP cheat will keep working — it''s the structurally easier cheat category — but the user-side discipline required to keep ESP unbanned will rise. Server-side culling will tighten further. Behavioral ML will get better at correlating movement with information. The casual "show every enemy on screen always" wallhack is in long-term decline; the careful, narrow, behaviorally-disciplined ESP user remains hard to catch and likely will for years.