Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection

• URL: http://arxiv.org/abs/2410.21337v1
• Date: Mon, 28 Oct 2024 00:36:21 GMT
• Title: Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection
• Authors: Md Abdur Rahman, Fan Wu, Alfredo Cuzzocrea, Sheikh Iqbal Ahamed,
• Abstract summary: Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. This project explores the security vulnerabilities in relation to prompt injection attacks.
• Score: 6.269725911814401
• License:
• Abstract: Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13\% accuracy, 100\% precision, 98.33\% recall and 99.15\% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks.

Related papers

• Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
  Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks. We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction. We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2024-11-01T04:05:59Z)
• Palisade -- Prompt Injection Detection Framework [0.9620910657090188]
  Large Language Models are vulnerable to malicious prompt injection attacks. This paper proposes a novel NLP based approach for prompt injection detection. It emphasizes accuracy and optimization through a layered input screening process.
  arXiv  Detail & Related papers  (2024-10-28T15:47:03Z)
• Aligning LLMs to Be Robust Against Prompt Injection [55.07562650579068]
  We show that alignment can be a powerful tool to make LLMs more robust against prompt injection attacks. Our method -- SecAlign -- first builds an alignment dataset by simulating prompt injection attacks. Our experiments show that SecAlign robustifies the LLM substantially with a negligible hurt on model utility.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• PROMPTFUZZ: Harnessing Fuzzing Techniques for Robust Testing of Prompt Injection in LLMs [16.296171008281775]
  Large Language Models (LLMs) have gained widespread use in various applications due to their powerful capability to generate human-like text. prompt injection attacks involve overwriting a model's original instructions with malicious prompts to manipulate the generated text. We propose PROMPTFUZZ, a novel testing framework that leverages fuzzing techniques to assess the robustness of LLMs against prompt injection attacks.
  arXiv  Detail & Related papers  (2024-09-23T06:08:32Z)
• Detecting AI Flaws: Target-Driven Attacks on Internal Faults in Language Models [27.397408870544453]
  Large Language Models (LLMs) have become a focal point in the rapidly evolving field of artificial intelligence. A critical concern is the presence of toxic content within the pre-training corpus of these models, which can lead to the generation of inappropriate outputs. This paper proposes a target-driven attack paradigm that focuses on directly eliciting the target response instead of optimizing the prompts.
  arXiv  Detail & Related papers  (2024-08-27T08:12:08Z)
• Are you still on track!? Catching LLM Task Drift with Activations [55.75645403965326]
  Task drift allows attackers to exfiltrate data or influence the LLM's output for other users. We show that a simple linear classifier can detect drift with near-perfect ROC AUC on an out-of-distribution test set. We observe that this approach generalizes surprisingly well to unseen task domains, such as prompt injections, jailbreaks, and malicious instructions.
  arXiv  Detail & Related papers  (2024-06-02T16:53:21Z)
• Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs [61.04246774006429]
  We introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent. We observe that our instruction-based prompts generate outputs with 23.7% higher overlap with training data compared to the baseline prefix-suffix measurements. Our findings show that instruction-tuned models can expose pre-training data as much as their base-models, if not more so, and using instructions proposed by other LLMs can open a new avenue of automated attacks.
  arXiv  Detail & Related papers  (2024-03-05T19:32:01Z)
• Attacking Large Language Models with Projected Gradient Descent [12.130638442765857]
  Projected Gradient Descent (PGD) for adversarial prompts is up to one order of magnitude faster than state-of-the-art discrete optimization. Our PGD for LLMs is up to one order of magnitude faster than state-of-the-art discrete optimization to achieve the same devastating attack results.
  arXiv  Detail & Related papers  (2024-02-14T13:13:26Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection [70.28425745910711]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following. This capability brings with it the risk of prompt injection attacks. We evaluate the robustness of instruction-following LLMs against such attacks.
  arXiv  Detail & Related papers  (2023-08-17T06:21:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.