Indoor positioning systems (IPSes) can enable many location-based services in large indoor venues where GPS signals are unavailable or unreliable. Among the most viable types of IPSes, RSS-IPSes rely on ubiquitous smartphones and indoor WiFi infrastructures and explore distinguishable received signal strength (RSS) measurements at different indoor locations as their location fingerprints. RSSIPSes are unfortunately vulnerable to physical-layer RSS attacks that cannot be thwarted by conventional cryptographic techniques. Existing defenses against RSS attacks are all subject to an inherent tradeoff between indoor positioning accuracy and attack resilience. This paper presents the design and evaluation of MV-IPS, a novel RSS-IPS based on weighted multi-voting, which does not suffer from this tradeoff. In MV-IPS, every WiFi access point (AP) that receives a user's RSS measurement gives a weighted vote for every reference location, and the reference location that receives the highest accumulative votes from all APs is output as the user's most likely position. Trace-driven simulation studies based on real RSS measurements demonstrate that MV-IPS can achieve much higher positioning accuracy than prior solutions no matter whether RSS attacks are present.