Sequential　recommendation　is　of　paramount　importance　for　predicting　user　preferences　based　on　their　historical　interactions.　Recent　studies　have　leveraged　contrastive　learning　as　an　auxiliary　task　to　enhance　sequence　representations,　with　the　goal　of　improving　recommendation　accuracy.　However,　an　important　challenge　arises:　random　item　masking,　a　key　component　of　contrastive　learning,　while　promoting　robust　representations　through　intricate　semantic　inference,　may　inadvertently　distort　the　original　sequence　semantics　to　some　extent.　In　contrast,　methods　that　prioritize　the　preservation　of　sequence　semantics　tend　to　neglect　the　essential　masking　mechanism　for　robust　representation　learning.　To　address　this　issue,　we　propose　a　model　called　Contrastive　Learning　with　Adversarial　Masking　(CLAM)　for　sequential　recommendation.　CLAM　consists　of　three　core　components:　an　inference　module,　an　occlusion　module,　and　a　multi-task　learning　paradigm.　During　training,　the　occlusion　module　is　optimized　to　perturb　the　inference　module　in　both　recommendation　generation　and　contrastive　learning　tasks　by　adaptively　generating　item　embedding　masks.　This　adversarial　training　framework　enables　CLAM　to　balance　sequential　pattern　preservation　with　the　acquisition　of　robust　representations　in　the　inference　module　for　recommendation　tasks.　Our　extensive　experiments　on　four　benchmark　datasets　demonstrate　the　effectiveness　of　CLAM.　It　achieves　significant　improvements　in　sequential　recommendation　accuracy　and　robustness　against　noisy　interactions.