diff --git a/src/generators.cpp b/src/generators.cpp
index 3657810442..388e63501e 100644
--- a/src/generators.cpp
+++ b/src/generators.cpp
@@ -420,6 +420,8 @@ void Generator::InitializeSamplingMethod(const GeneratorParams& params) {
       throw std::runtime_error("top_p must be between 0.0 and 1.0");
     if (search.top_k < 0)
       throw std::runtime_error("top_k must be 0 or greater");
+    if (search.top_k > params.config.model.vocab_size)
+      throw std::runtime_error("top_k (" + std::to_string(search.top_k) + ") must be less than or equal to vocab_size (" + std::to_string(params.config.model.vocab_size) + ")");
     if (search.top_p > 0.0f && search.top_p < 1.0f && search.top_k > 1) {
       sampling_method_ = SamplingMethod::kTopKTopP;
     } else if (search.top_k > 1) {
diff --git a/src/search.cpp b/src/search.cpp
index bbc27644c2..b8ca196e60 100644
--- a/src/search.cpp
+++ b/src/search.cpp
@@ -170,6 +170,7 @@ void GreedySearch_Cpu::SelectTop() {
 
 void GreedySearch_Cpu::SampleTopK(int k, float temperature) {
   const int vocab_size = params_->config.model.vocab_size;
+  k = std::min(k, vocab_size);
   std::vector<int> indices(vocab_size);
   std::vector<float> top_k_scores(k);
 
@@ -329,6 +330,9 @@ void GreedySearch_Cpu::SampleTopP(float p, float temperature) {
 void GreedySearch_Cpu::SampleTopKTopP(int k, float p, float temperature) {
   assert(temperature > 0.0f);
 
+  // Clamp k to vocab_size to prevent out-of-bounds access in partial_sort
+  k = std::min(k, params_->config.model.vocab_size);
+
   // --- Buffers allocated once to avoid re-allocations in the batch loop ---
   std::vector<int32_t> indices(params_->config.model.vocab_size);
 
diff --git a/test/sampling_tests.cpp b/test/sampling_tests.cpp
index 60bac47f40..afca86345a 100644
--- a/test/sampling_tests.cpp
+++ b/test/sampling_tests.cpp
@@ -460,6 +460,19 @@ TEST(SamplingTests, RepetitionPenaltyCorrectnessCpu) {
       << " Unpenalized per-token avg=" << unpenalized_avg;
 }
 
+TEST(SamplingTests, TopKExceedingVocabSizeIsRejected) {
+  auto config = OgaConfig::Create(MODEL_PATH "hf-internal-testing/tiny-random-gpt2-fp32");
+  config->ClearProviders();
+  auto model = OgaModel::Create(*config);
+
+  auto params = OgaGeneratorParams::Create(*model);
+  params->SetSearchOption("max_length", 25);
+  params->SetSearchOptionBool("do_sample", true);
+  params->SetSearchOption("top_k", 5000);  // vocab_size is 1000
+
+  EXPECT_THROW(OgaGenerator::Create(*model, *params), std::runtime_error);
+}
+
 #if USE_CUDA
 TEST(SamplingTests, BatchedSamplingTopPCuda) {
   std::vector<int32_t> input_ids{0, 1, 2, 3};
@@ -480,6 +493,7 @@ TEST(SamplingTests, BatchedSamplingTopPCuda) {
   auto params = OgaGeneratorParams::Create(*model);
   params->SetSearchOption("max_length", 10);
   params->SetSearchOptionBool("do_sample", true);
+  params->SetSearchOption("top_k", 1);
   params->SetSearchOption("top_p", 0.25f);
   params->SetSearchOption("batch_size", batch_size);