diff --git a/src/derivative_wrappers.jl b/src/derivative_wrappers.jl
index 3f71df703..cbded0b1c 100644
--- a/src/derivative_wrappers.jl
+++ b/src/derivative_wrappers.jl
@@ -693,10 +693,10 @@ function _vecjacobian!(dλ, y, λ, p, t, S::TS, isautojacvec::EnzymeVJP, dgrad,
                 dup,
                 Enzyme.Const(t), Enzyme.Const(W))
         end
-        dλ !== nothing && (dλ .= tmp1)
+        dλ !== nothing && recursive_copyto!(dλ,tmp1)
         dgrad !== nothing && !(typeof(tmp2) <: DiffEqBase.NullParameters) &&
             recursive_copyto!(dgrad, tmp2)
-        dy !== nothing && (dy .= tmp3)
+        dy !== nothing && recursive_copyto!(dy,tmp3)
     else
         if W === nothing
             Enzyme.autodiff(Enzyme.Reverse, S.diffcache.pf, Enzyme.Duplicated(tmp3, tmp4),
@@ -715,10 +715,10 @@ function _vecjacobian!(dλ, y, λ, p, t, S::TS, isautojacvec::EnzymeVJP, dgrad,
             end
             recursive_copyto!(dy, out_)
         end
-        dλ !== nothing && (dλ .= tmp1)
+        dλ !== nothing && recursive_copyto!(dλ,tmp1)
         dgrad !== nothing && !(typeof(tmp2) <: DiffEqBase.NullParameters) &&
             recursive_copyto!(dgrad, tmp2)
-        dy !== nothing && (dy .= tmp3)
+        dy !== nothing && recursive_copyto!(dy,tmp3)
     end
     return
 end
diff --git a/test/size_handling_adjoint.jl b/test/size_handling_adjoint.jl
index 94717551c..d8c139ef7 100644
--- a/test/size_handling_adjoint.jl
+++ b/test/size_handling_adjoint.jl
@@ -1,4 +1,4 @@
-using SciMLSensitivity, Flux, OrdinaryDiffEq, Test # , Plots
+using SciMLSensitivity, Zygote, Flux, OrdinaryDiffEq, Test # , Plots
 
 p = [1.5 1.0; 3.0 1.0]
 function lotka_volterra(du, u, p, t)
@@ -36,3 +36,38 @@ cb()
 Flux.train!(loss_adjoint, ps, data, opt, cb = cb)
 
 @test loss_adjoint() < 1
+
+tspan = (0, 1)
+tran = collect(0:0.1:1)
+p0 = rand(2)
+f0 = randn(30, 50)
+
+function rhs!(df, f, p, t)
+    for j in axes(f, 2)
+        for i in axes(f, 1)
+            df[i, j] = p[1] * i + p[2] * j
+        end
+    end
+    return nothing
+end
+
+function loss(p; vjp)
+    prob = ODEProblem(rhs!, f0, tspan, p)
+    sol = solve(prob, Midpoint(), saveat = tran, sensealg=InterpolatingAdjoint(autojacvec=vjp)) |> Array
+    l = sum(abs2, sol)
+
+    return l
+end
+
+dp1 = Zygote.pullback(x -> loss(x; vjp = EnzymeVJP()), p0)[2](1)[1]
+dp2 = Zygote.pullback(x -> loss(x; vjp = ReverseDiffVJP()), p0)[2](1)[1]
+dp3 = Zygote.pullback(x -> loss(x; vjp = TrackerVJP()), p0)[2](1)[1]
+dp4 = Zygote.pullback(x -> loss(x; vjp = EnzymeVJP()), p0)[2](1)[1]
+dp5 = Zygote.pullback(x -> loss(x; vjp = true), p0)[2](1)[1]
+dp6 = Zygote.pullback(x -> loss(x; vjp = false), p0)[2](1)[1]
+
+@test dp1 ≈ dp2
+@test dp1 ≈ dp3
+@test dp1 ≈ dp4
+@test dp1 ≈ dp5
+@test dp1 ≈ dp6
\ No newline at end of file