diff --git a/DifferentiationInterface/ext/DifferentiationInterfaceSparseMatrixColoringsExt/hessian.jl b/DifferentiationInterface/ext/DifferentiationInterfaceSparseMatrixColoringsExt/hessian.jl
index 495b31b10..f5d674cc4 100644
--- a/DifferentiationInterface/ext/DifferentiationInterfaceSparseMatrixColoringsExt/hessian.jl
+++ b/DifferentiationInterface/ext/DifferentiationInterfaceSparseMatrixColoringsExt/hessian.jl
@@ -4,6 +4,7 @@ struct SMCSparseHessianPrep{
         P <: AbstractMatrix,
         C <: AbstractColoringResult{:symmetric, :column},
         M <: AbstractMatrix{<:Number},
+        Sp <: NTuple,
         S <: AbstractVector{<:NTuple},
         R <: AbstractVector{<:NTuple},
         E2 <: DI.HVPPrep,
@@ -14,6 +15,7 @@ struct SMCSparseHessianPrep{
     sparsity::P
     coloring_result::C
     compressed_matrix::M
+    batched_seed_prep::Sp
     batched_seeds::S
     batched_results::R
     hvp_prep::E2
@@ -54,14 +56,20 @@ function _prepare_sparse_hessian_aux(
     (; N, A) = batch_size_settings
     dense_backend = dense_ad(backend)
     groups = column_groups(coloring_result)
+    seed_prep = DI.multibasis(x, eachindex(x))
     seeds = [DI.multibasis(x, eachindex(x)[group]) for group in groups]
-    compressed_matrix = stack(_ -> vec(similar(x)), groups; dims = 2)
+    compressed_matrix = if isempty(groups)
+        similar(x, length(x), 0)
+    else
+        stack(_ -> vec(similar(x)), groups; dims = 2)
+    end
+    batched_seed_prep = ntuple(b -> copy(seed_prep), Val(B))
     batched_seeds = [
         ntuple(b -> seeds[1 + ((a - 1) * B + (b - 1)) % N], Val(B)) for a in 1:A
     ]
     batched_results = [ntuple(b -> similar(x), Val(B)) for _ in batched_seeds]
     hvp_prep = DI.prepare_hvp_nokwarg(
-        strict, f, dense_backend, x, batched_seeds[1], contexts...
+        strict, f, dense_backend, x, batched_seed_prep, contexts...
     )
     gradient_prep = DI.prepare_gradient_nokwarg(
         strict, f, DI.inner(dense_backend), x, contexts...
@@ -72,6 +80,7 @@ function _prepare_sparse_hessian_aux(
         sparsity,
         coloring_result,
         compressed_matrix,
+        batched_seed_prep,
         batched_seeds,
         batched_results,
         hvp_prep,
@@ -92,6 +101,7 @@ function DI.hessian!(
         batch_size_settings,
         coloring_result,
         compressed_matrix,
+        batched_seed_prep,
         batched_seeds,
         batched_results,
         hvp_prep,
@@ -100,7 +110,7 @@ function DI.hessian!(
     dense_backend = dense_ad(backend)
 
     hvp_prep_same = DI.prepare_hvp_same_point(
-        f, hvp_prep, dense_backend, x, batched_seeds[1], contexts...
+        f, hvp_prep, dense_backend, x, batched_seed_prep, contexts...
     )
 
     for a in eachindex(batched_seeds, batched_results)
diff --git a/DifferentiationInterface/test/Core/SimpleFiniteDiff/test.jl b/DifferentiationInterface/test/Core/SimpleFiniteDiff/test.jl
index 0d8d0f703..1bd40bfa6 100644
--- a/DifferentiationInterface/test/Core/SimpleFiniteDiff/test.jl
+++ b/DifferentiationInterface/test/Core/SimpleFiniteDiff/test.jl
@@ -136,7 +136,16 @@ end
         @test only(column_groups(hess_prep)) == 1:10
     end
 
-    @testset "Empty colors for mixed mode" begin # issue 857
+    @testset "Empty color groups in sparse AD" begin # issue 857
+        # forward
+        backend = MyAutoSparse(adaptive_backends[1])
+        @test jacobian(zero, backend, ones(10)) isa AbstractMatrix
+        @test hessian(sum ∘ zero, backend, ones(10)) isa AbstractMatrix
+        # reverse
+        backend = MyAutoSparse(adaptive_backends[2])
+        @test jacobian(zero, backend, ones(10)) isa AbstractMatrix
+        @test hessian(sum ∘ zero, backend, ones(10)) isa AbstractMatrix
+        # mixed
         backend = MyAutoSparse(MixedMode(adaptive_backends[1], adaptive_backends[2]))
         @test jacobian(copyto!, zeros(10), backend, ones(10)) isa AbstractMatrix
     end