# A Novel Evaluation Framework for Assessing Resilience Against Prompt Injection Attacks in Large Language Models

arXiv: https://arxiv.org/abs/2401.00991 · HTML: https://mlsanity.com/paper/2401.00991/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 15 of 15)

- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://mlsanity.com/paper/2305.13860/) (2023)
- [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://mlsanity.com/paper/2304.05197/) (2023)
- [Visual Adversarial Examples Jailbreak Aligned Large Language Models](https://mlsanity.com/paper/2306.13213/) (2023)
- [Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks](https://mlsanity.com/paper/2302.05733/) (2023)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems](https://mlsanity.com/paper/1903.11508/) (2019)
- [Generating Phishing Attacks using ChatGPT](https://mlsanity.com/paper/2305.05133/) (2023)

## Built on by (most cited first, 15 of 15)

- [StruQ: Defending Against Prompt Injection with Structured Queries](https://mlsanity.com/paper/2402.06363/) (2024)
- [SecAlign: Defending Against Prompt Injection with Preference Optimization](https://mlsanity.com/paper/2410.05451/) (2024)
- [COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability](https://mlsanity.com/paper/2402.08679/) (2024)
- [Automatic and Universal Prompt Injection Attacks against Large Language Models](https://mlsanity.com/paper/2403.04957/) (2024)
- [System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective](https://mlsanity.com/paper/2409.19091/) (2024)
- [A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems](https://mlsanity.com/paper/2402.18649/) (2024)
- [WIPI: A New Web Threat for LLM-Driven Web Agents](https://mlsanity.com/paper/2402.16965/) (2024)
- [Security Threats in Agentic AI System](https://mlsanity.com/paper/2410.14728/) (2024)
- [A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks](https://mlsanity.com/paper/2509.14285/) (2025)
- [When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins](https://mlsanity.com/paper/2511.05797/) (2025)
- [Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection](https://mlsanity.com/paper/2409.13331/) (2024)
- [A Call to Action for a Secure-by-Design Generative AI Paradigm](https://mlsanity.com/paper/2510.00451/) (2025)
- [MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation](https://mlsanity.com/paper/2510.10271/) (2025)
- [Automating Cloud Security and Forensics Through a Secure-by-Design Generative AI Framework](https://mlsanity.com/paper/2604.03912/) (2026)
- [Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward](https://mlsanity.com/paper/2601.13122/) (2026)

## Abstract (arXiv)

Prompt injection attacks exploit vulnerabilities in large language models (LLMs) to manipulate the model into unintended actions or generate malicious content. As LLM integrated applications gain wider adoption, they face growing susceptibility to such attacks. This study introduces a novel evaluation framework for quantifying the resilience of applications. The framework incorporates innovative techniques designed to ensure representativeness, interpretability, and robustness. To ensure the representativeness of simulated attacks on the application, a meticulous selection process was employed, resulting in 115 carefully chosen attacks based on coverage and relevance. For enhanced interpretability, a second LLM was utilized to evaluate the responses generated from these simulated attacks. Unlike conventional malicious content classifiers that provide only a confidence score, the LLM-based evaluation produces a score accompanied by an explanation, thereby enhancing interpretability. Subsequently, a resilience score is computed by assigning higher weights to attacks with greater impact, thus providing a robust measurement of the application resilience. To assess the framework's efficacy, it was applied on two LLMs, namely Llama2 and ChatGLM. Results revealed that Llama2, the newer model exhibited higher resilience compared to ChatGLM. This finding substantiates the effectiveness of the framework, aligning with the prevailing notion that newer models tend to possess greater resilience. Moreover, the framework exhibited exceptional versatility, requiring only minimal adjustments to accommodate emerging attack techniques and classifications, thereby establishing itself as an effective and practical solution. Overall, the framework offers valuable insights that empower organizations to make well-informed decisions to fortify their applications against potential threats from prompt injection.