Llama Models Security: From OWASPs Benchmarking to Encoding-based obfuscation attacks
As large language models (LLMs) are deployed in security-critical environments, evaluating their robustness against adversarial threats is essential. We present a two-phase evaluation of ten Llama variants using the OWASP Top 10 for LLM Applications. In Phase I we benchmark 100 OWASP-based adversarial prompts; Llama-Guard-3-1B leads (76% detection, 0.165 s latency, 0.94 GB VRAM), while base models detect nothing. In Phase II we apply nine encoding obfuscations to the same prompts (9,000 inferences): Base64 stays detectable (up to 100%), hexadecimal collapses to 2%, and ASCII art fully bypasses all text-only models. Model scale alone does not ensure robustness; targeted safety training and encoding-aware defenses are required. Both benchmark datasets are released for reproducibility [3].