Large language models (LLMs) are increasingly integrated into embodied robotic planning, yet standard benchmarks primarily evaluate end-to-end task completion in nominal environments, obscuring failure origins across the execution stack. We present Alpha, a systematic fault-injection and robustness-evaluation framework for LLM-driven manipulation in simulation. Alpha injects calibrated faults across perception (sensor lag), actuation (grip slip), kinematics (unreachable targets), and planning (planner-output corruption), alongside concurrent conditions. To separate physical vulnerabilities from planner sensitivities, we introduce a matched methodology pairing an LLM agent (Llama-3 via Ollama) against a deterministic Scripted Oracle planner across a balanced 150-episode matrix under identical seeds, geometries, and safety criteria. Across the matrix, the Oracle achieves 64.0% (48/75) success while the LLM achieves 44.0% (33/75) (paired McNemar p = 3.01 x 10^-4). Crucially, excluding planner-output corruption, performance across all physical and kinematic conditions is identical: 55.0% (33/60) for both planners with zero discordant pairs (McNemar p = 1.00). This demonstrates that physical actuator slip, kinematic reach boundaries, and perceptual staleness affect deterministic controllers and LLM planners equivalently. The performance gap is driven entirely by planner-output corruption (100% Oracle vs. 0% LLM), isolating the prompt-to-JSON serialization and validation interface rather than a cognitive reasoning deficit. We also audit 169 historical pilot attempts for transparency.

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