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Analysis of LLMs Against Prompt Injection and Jailbreak Attacks
arXiv – CS AI|Piyush Jaiswal, Aaditya Pratap, Shreyansh Saraswati, Harsh Kasyap, Somanath Tripathy||7 views
🤖AI Summary
Researchers evaluated prompt injection and jailbreak vulnerabilities across multiple open-source LLMs including Phi, Mistral, DeepSeek-R1, Llama 3.2, Qwen, and Gemma. The study found significant behavioral variations across models and that lightweight defense mechanisms can be consistently bypassed by long, reasoning-heavy prompts.
Key Takeaways
- →Multiple open-source LLMs show significant behavioral variations when subjected to prompt-based attacks.
- →Models exhibit different responses including refusal responses and complete silent non-responsiveness due to internal safety mechanisms.
- →Lightweight inference-time defense mechanisms can mitigate straightforward attacks without requiring retraining or GPU-intensive fine-tuning.
- →These defense mechanisms are consistently bypassed by long, reasoning-heavy prompts.
- →The research highlights critical security requirements for organizations deploying LLMs in real-world systems.
#llm-security#prompt-injection#jailbreak-attacks#ai-safety#vulnerability-research#open-source-llms#defense-mechanisms
Read Original →via arXiv – CS AI
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