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With The Popularity Of Voting Systems In Cyberspace, There Is Growing Evidence That Current Voting Systems Can Be Manipulated By Fake Votes. This Problem Has Attracted Many Researchers Working On Guarding Voting Systems In Two Areas: Relieving The Effect Of Dishonest Votes By Evaluating The Trust Of Voters, And Limiting The Resources That Can Be Used By Attackers, Such As The Number Of Voters And The Number Of Votes. In This Paper, We Argue That Powering Voting Systems With Trust And Limiting Attack Resources Are Not Enough. We Present A Novel Attack Named As Reputation Trap (RepTrap). Our Case Study And Experiments Show That This New Attack Needs Much Less Resources To Manipulate The Voting Systems And Has A Much Higher Success Rate Compared With Existing Attacks. We Further Identify The Reasons Behind This Attack And Propose Two Defense Schemes Accordingly. In The First Scheme, We Hide Correlation Knowledge From Attackers To Reduce Their Chance To Affect The Honest Voters. In The Second Scheme, We Introduce Robustness-of-evidence, A New Metric, In Trust Calculation To Reduce Their Effect On Honest Voters. We Conduct Extensive Experiments To Validate Our Approach. The Results Show That Our Defense Schemes Not Only Can Reduce The Success Rate Of Attacks But Also Significantly Increase The Amount Of Resources An Adversary Needs To Launch A Successful Attack.