Agentic fact-checking (FC) has emerged as a powerful paradigm for verifying complex claims through dynamic reasoning, tool use, and iterative evidence gathering. Understanding the vulnerability of these systems is essential for assessing their reliability against misinformation and adversarial manipulation. In this work, we study both the offensive and defensive aspects of agentic FC. First, we identify a limitation in the state-of-the-art Fact2Fiction attack, which introduces excessive semantic drift into malicious evidence, and propose F+, a relevance-filtering framework that generates more semantically consistent adversarial evidence. Second, we introduce SRSFact, an enhanced agentic FC system with a Supporter–Refuter–Summarizer mechanism for structured evidence authenticity assessment, improving robustness without relying on external open-web knowledge search. Experiments on the AVeriTeC benchmark show that F+ significantly strengthens attack effectiveness, while SRSFact substantially reduces failure rates under diverse adversarial attacks.