Large language models (LLMs) are increasingly used in interactive and retrieval-augmented systems, but they remain vulnerable to task drift; deviations from a user's intended instruction due to injected secondary prompts. Recent work has shown that linear probes trained on activation deltas of LLMs' hidden layers can effectively detect such drift. In this paper, we evaluate the robustness of these detectors against adversarially optimised suffixes. We generate universal suffixes that cause poisoned inputs to evade detection across multiple probes simultaneously. Our experiments on Phi-3 3.8B and Llama-3 8B show that a single suffix can achieve high attack success rates; up to 93.91% and 99.63%, respectively, when all probes must be fooled, and nearly perfect success (>90%) under majority vote setting. These results demonstrate that activation delta-based task drift detectors are highly vulnerable to adversarial suffixes, highlighting the need for stronger defences against adaptive attacks. We also propose a defence technique where we generate multiple suffixes and randomly append one of them to the prompts while making forward passes of the LLM and train logistic regression models with these activations. We found this approach to be highly effective against such attacks.