diff --git a/R/check_collinearity.R b/R/check_collinearity.R
index 86a28334a..c3a449084 100644
--- a/R/check_collinearity.R
+++ b/R/check_collinearity.R
@@ -481,6 +481,49 @@ check_collinearity.zerocount <- function(
     return(NULL)
   }
 
+  # Filter to true slope parameters (handles multiple intercepts in ordinal models)
+  if (inherits(x, c("clm", "clmm"))) {
+    # names(x$beta) returns only non-singular (surviving) slopes
+    slope_names <- names(x$beta)
+    keep_idx <- which(colnames(v) %in% slope_names)
+
+    # Rebuild term_assign by matching model matrix columns to surviving slopes
+    tryCatch(
+      {
+        mm <- insight::get_modelmatrix(x)
+        assign_attr <- attr(mm, "assign")
+        if (!is.null(assign_attr)) {
+          # Use name-matching to isolate indices for estimated slopes
+          match_idx <- which(colnames(mm) %in% slope_names)
+          if (length(match_idx) > 0) {
+            term_assign <- assign_attr[match_idx]
+          }
+        }
+      },
+      error = function(e) NULL
+    )
+  } else if (insight::has_intercept(x)) {
+    # Standard behavior: drop the first column/row (the singular intercept)
+    keep_idx <- seq_len(ncol(v))[-1]
+  } else {
+    keep_idx <- seq_len(ncol(v))
+    if (isTRUE(verbose)) {
+      insight::format_alert("Model without intercept. VIFs may not be sensible.")
+    }
+  }
+
+  # Safely subset the matrix (term_assign is already synced for ordinal models)
+  if (length(keep_idx) < ncol(v)) {
+    if (
+      !inherits(x, c("clm", "clmm")) &&
+        !is.null(term_assign) &&
+        length(term_assign) == ncol(v)
+    ) {
+      term_assign <- term_assign[keep_idx]
+    }
+    v <- v[keep_idx, keep_idx, drop = FALSE]
+  }
+
   # we have rank-deficiency here. remove NA columns from assignment
   if (isTRUE(attributes(v)$rank_deficient) && !is.null(attributes(v)$na_columns_index)) {
     term_assign <- term_assign[-attributes(v)$na_columns_index]
@@ -491,14 +534,6 @@ check_collinearity.zerocount <- function(
     }
   }
 
-  # check for missing intercept
-  if (insight::has_intercept(x)) {
-    v <- v[-1, -1]
-    term_assign <- term_assign[-1]
-  } else if (isTRUE(verbose)) {
-    insight::format_alert("Model has no intercept. VIFs may not be sensible.")
-  }
-
   f <- insight::find_formula(x, verbose = FALSE)
 
   # hurdle or zeroinfl model can have no zero-inflation formula, in which case