PIArena: A Platform for Prompt Injection Evaluation

• URL: http://arxiv.org/abs/2604.08499v1
• Date: Thu, 09 Apr 2026 17:42:45 GMT
• Title: PIArena: A Platform for Prompt Injection Evaluation
• Abstract summary: Prompt injection attacks pose serious security risks across a wide range of real-world applications.<n>The community faces a critical gap: the lack of a unified platform for prompt injection evaluation.<n>PIArena enables users to easily integrate state-of-the-art attacks and defenses and evaluate them across a variety of existing and new benchmarks.
• Score: 21.281542161531934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt injection attacks pose serious security risks across a wide range of real-world applications. While receiving increasing attention, the community faces a critical gap: the lack of a unified platform for prompt injection evaluation. This makes it challenging to reliably compare defenses, understand their true robustness under diverse attacks, or assess how well they generalize across tasks and benchmarks. For instance, many defenses initially reported as effective were later found to exhibit limited robustness on diverse datasets and attacks. To bridge this gap, we introduce PIArena, a unified and extensible platform for prompt injection evaluation that enables users to easily integrate state-of-the-art attacks and defenses and evaluate them across a variety of existing and new benchmarks. We also design a dynamic strategy-based attack that adaptively optimizes injected prompts based on defense feedback. Through comprehensive evaluation using PIArena, we uncover critical limitations of state-of-the-art defenses: limited generalizability across tasks, vulnerability to adaptive attacks, and fundamental challenges when an injected task aligns with the target task. The code and datasets are available at https://github.com/sleeepeer/PIArena.

Related papers

• LongPIBench: A Long-Context Benchmark for Prompt Injection [52.0498674763688]
  LongPIBench is a benchmark for evaluating prompt injection defenses in long-context scenarios.<n>We show that even simple prompt injection attacks achieve high success rates and frequently bypass state-of-the-art defenses.
  arXiv  Detail & Related papers  (2026-08-28T15:00:33Z)
• ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack [52.17935054046577]
  We present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks.<n>ReasAlign incorporates structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks.
  arXiv  Detail & Related papers  (2026-01-15T08:23:38Z)
• Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks? [58.48689960350828]
  We show that a simple, modular and model-agnostic defense operating at the agent--tool interface achieves perfect security with high utility.<n>We employ a defense based on two firewalls: a Tool-Input Firewall (Minimizer) and a Tool-Output Firewall (Sanitizer)
  arXiv  Detail & Related papers  (2025-10-06T18:09:02Z)
• PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance [10.105673138616483]
  Large Language Models (LLMs) are increasingly integrated into real-world applications, from virtual assistants to autonomous agents.<n>As attackers evolve with paraphrased, obfuscated, and even multi-task injection strategies, existing benchmarks are no longer sufficient to capture the full spectrum of emerging threats.<n>We propose PromptSleuth, a semantic-oriented defense framework that detects prompt injection by reasoning over task-level intent rather than surface features.
  arXiv  Detail & Related papers  (2025-08-28T15:19:07Z)
• TopicAttack: An Indirect Prompt Injection Attack via Topic Transition [92.26240528996443]
  Large language models (LLMs) are vulnerable to indirect prompt injection attacks.<n>We propose TopicAttack, which prompts the LLM to generate a fabricated transition prompt that gradually shifts the topic toward the injected instruction.<n>We find that a higher injected-to-original attention ratio leads to a greater success probability, and our method achieves a much higher ratio than the baseline methods.
  arXiv  Detail & Related papers  (2025-07-18T06:23:31Z)
• Benchmarking Misuse Mitigation Against Covert Adversaries [80.74502950627736]
  Existing language model safety evaluations focus on overt attacks and low-stakes tasks.<n>We develop Benchmarks for Stateful Defenses (BSD), a data generation pipeline that automates evaluations of covert attacks and corresponding defenses.<n>Our evaluations indicate that decomposition attacks are effective misuse enablers, and highlight stateful defenses as a countermeasure.
  arXiv  Detail & Related papers  (2025-06-06T17:33:33Z)
• OET: Optimization-based prompt injection Evaluation Toolkit [25.148709805243836]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation.<n>Their susceptibility to prompt injection attacks poses significant security risks.<n>Despite numerous defense strategies, a standardized framework to rigorously evaluate their effectiveness is lacking.
  arXiv  Detail & Related papers  (2025-05-01T20:09:48Z)
• Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents [3.5248694676821484]
  We evaluate eight different defenses and bypass all of them using adaptive attacks, consistently achieving an attack success rate of over 50%.<n>Our research underscores the need for adaptive attack evaluation when designing defenses to ensure robustness and reliability.
  arXiv  Detail & Related papers  (2025-02-27T04:04:50Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

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.