diff --git a/Project.toml b/Project.toml
index 28fec35..21a79c2 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "SliceSampling"
 uuid = "43f4d3e8-9711-4a8c-bd1b-03ac73a255cf"
-version = "0.7.7"
+version = "0.7.8"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -21,7 +21,7 @@ Distributions = "0.25"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 Random = "1"
-Turing = "0.39.5"
+Turing = "0.40"
 julia = "1.10"
 
 [extras]
diff --git a/docs/Project.toml b/docs/Project.toml
index 65b7f91..845e9b0 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -28,5 +28,5 @@ Random = "1"
 SliceSampling = "0.7.1"
 StableRNGs = "1"
 Statistics = "1"
-Turing = "0.37, 0.38, 0.39"
+Turing = "0.37, 0.38, 0.39, 0.40"
 julia = "1.10"
diff --git a/ext/SliceSamplingTuringExt.jl b/ext/SliceSamplingTuringExt.jl
index 44f9f6a..d20fc71 100644
--- a/ext/SliceSamplingTuringExt.jl
+++ b/ext/SliceSamplingTuringExt.jl
@@ -1,6 +1,7 @@
 
 module SliceSamplingTuringExt
 
+using LogDensityProblems
 using Random
 using SliceSampling
 using Turing
@@ -32,32 +33,38 @@ const SliceSamplingStates = Union{
 function Turing.Inference.getparams(::Turing.DynamicPPL.Model, sample::SliceSamplingStates)
     return sample.transition.params
 end
-
-function Turing.Inference.getlogp_external(
-    ::Turing.DynamicPPL.Model, t::SliceSampling.Transition, state
-)
-    return t.lp
-end
 # end
 
 function SliceSampling.initial_sample(rng::Random.AbstractRNG, ℓ::Turing.LogDensityFunction)
+    n_max_attempts = 1000
+
     model  = ℓ.model
     vi     = Turing.DynamicPPL.VarInfo(rng, model, Turing.SampleFromUniform())
-    vi_spl = last(Turing.DynamicPPL.evaluate!!(model, rng, vi, Turing.SampleFromUniform()))
+    vi_spl = last(Turing.DynamicPPL.evaluate_and_sample!!(rng, model, vi, Turing.SampleFromUniform()))
     θ      = vi_spl[:]
+    ℓp     = LogDensityProblems.logdensity(ℓ, θ)
 
     init_attempt_count = 1
-    while !all(isfinite.(θ))
-        if init_attempt_count == 10
-            @warn "failed to find valid initial parameters in $(init_attempt_count) tries; consider providing explicit initial parameters using the `initial_params` keyword"
+    for attempts in 1:n_max_attempts
+        if attempts == 10
+            @warn "Failed to find valid initial parameters after $(init_attempt_count) attempts; consider providing explicit initial parameters using the `initial_params` keyword"
         end
 
         # NOTE: This will sample in the unconstrained space.
-        vi_spl = last(Turing.DynamicPPL.evaluate!!(model, rng, vi, Turing.SampleFromUniform()))
-        θ      = vi_spl[:]
+        vi_spl = last(
+            Turing.DynamicPPL.evaluate_and_sample!!(
+                rng, model, vi, Turing.SampleFromUniform()
+            ),
+        )
+        θ = vi_spl[:]
+        ℓp = LogDensityProblems.logdensity(ℓ, θ)
 
-        init_attempt_count += 1
+        if all(isfinite.(θ)) && isfinite(ℓp)
+            return θ
+        end
     end
+
+    @error "Failed to find valid initial parameters after $(n_max_attempts) attempts; consider providing explicit initial parameters using the `initial_params` keyword"
     return θ
 end
 
diff --git a/src/SliceSampling.jl b/src/SliceSampling.jl
index b8df323..c43875c 100644
--- a/src/SliceSampling.jl
+++ b/src/SliceSampling.jl
@@ -1,4 +1,4 @@
-ls
+
 module SliceSampling
 
 using AbstractMCMC
diff --git a/test/Project.toml b/test/Project.toml
index cadc38f..2c43400 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -18,5 +18,5 @@ MCMCTesting = "0.3"
 Random = "1"
 StableRNGs = "1"
 Test = "1"
-Turing = "0.37, 0.38, 0.39"
+Turing = "0.37, 0.38, 0.39, 0.40"
 julia = "1.10"
diff --git a/test/turing.jl b/test/turing.jl
index f5c6104..b0e1886 100644
--- a/test/turing.jl
+++ b/test/turing.jl
@@ -8,11 +8,30 @@
         return nothing
     end
 
+    @model function illbehavedmodel()
+        @addlogprob! -Inf
+        return nothing
+    end
+
     @model function logp_check()
         a ~ Normal()
         return b ~ Normal()
     end
 
+    rng = Random.default_rng()
+    @test begin
+        init = SliceSampling.initial_sample(rng, LogDensityFunction(demo()))
+        all(isfinite.(init))
+    end
+
+    @test_warn "Warning: Failed" SliceSampling.initial_sample(
+        rng, LogDensityFunction(illbehavedmodel())
+    )
+
+    @test_warn "Error: Failed" SliceSampling.initial_sample(
+        rng, LogDensityFunction(illbehavedmodel())
+    )
+
     n_samples = 1000
     model = demo()