GeneBreaker: Jailbreak Attacks against DNA Language Models with Pathogenicity Guidance

DNA, encoding genetic instructions for almost all living organisms, fuels groundbreaking advances in genomics and synthetic biology. Recently, DNA Language Models have achieved success in designing synthetic functional DNA sequences, even whole genomes of novel bacteriophage, verified with wet lab experiments. Such remarkable generative power also brings severe biosafety concerns about whether DNA language models can design human viruses. With the goal of exposing vulnerabilities and informing the development of robust safeguarding techniques, we perform a systematic biosafety evaluation of DNA language models through the lens of jailbreak attacks. Specifically, we introduce JailbreakDNABench, a benchmark centered on high-priority human viruses, together with an end-to-end jailbreak framework, GeneBreaker. GeneBreaker integrates three key components: (1) an LLM agent equipped with customized bioinformatics tools to design high-homology yet non-pathogenic jailbreak prompts, (2) beam search guided by PathoLM and log-probability heuristics to steer sequence generation toward pathogen-like outputs, and (3) a BLAST- and function-annotation–based evaluation pipeline to identify successful jailbreaks. On JailbreakDNABench, GeneBreaker successfully jailbreaks the latest Evo series models across 6 viral categories consistently (up to 60\% Attack Success Rate for Evo2-40B). Further case studies on SARS-CoV-2 spike protein and HIV-1 envelope protein demonstrate the sequence and structural fidelity of jailbreak output, while evolutionary modeling of SARS-CoV-2 underscores biosecurity risks. Our findings also reveal that scaling DNA language models amplifies dual-use risks, motivating enhanced safety alignment and tracing mechanisms.