Prompt Optimization and Evaluation for LLM Automated Red Teaming

• URL: http://arxiv.org/abs/2507.22133v1
• Date: Tue, 29 Jul 2025 18:08:01 GMT
• Title: Prompt Optimization and Evaluation for LLM Automated Red Teaming
• Abstract summary: We introduce a method for optimizing attack generator prompts that applies ASR to individual attacks.<n>By repeating each attack against a randomly seeded target, we measure an attack's discoverability the expectation of the individual attack success.
• Score: 0.5259346146529064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applications that use Large Language Models (LLMs) are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accelerates this effort by using an LLM to generate and execute attacks against target systems. Attack generators are evaluated using the Attack Success Rate (ASR) the sample mean calculated over the judgment of success for each attack. In this paper, we introduce a method for optimizing attack generator prompts that applies ASR to individual attacks. By repeating each attack multiple times against a randomly seeded target, we measure an attack's discoverability the expectation of the individual attack success. This approach reveals exploitable patterns that inform prompt optimization, ultimately enabling more robust evaluation and refinement of generators.

Related papers

• Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems [0.0]
  Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.<n>This work analyzes the resulting attack-defense setting through a probabilistic model of a target system, its defense mechanism, and the attacker's automated judge.
  arXiv  Detail & Related papers  (2026-06-18T16:50:28Z)
• Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment [25.752599132396437]
  Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution.<n>Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.<n>We propose RETA, a training-based method that grounds defense decisions on the user tasks rather than attacker-controlled data.
  arXiv  Detail & Related papers  (2026-06-13T19:15:44Z)
• Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems [51.504307822017985]
  Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance.<n>This paper investigates the item promotion attack in LLM-SRSs under a more realistic setting where both the system prompt and victim model are unknown to the attacker.
  arXiv  Detail & Related papers  (2026-04-26T10:09:26Z)
• Are My Optimized Prompts Compromised? Exploring Vulnerabilities of LLM-based Optimizers [21.207996237794855]
  We present the first systematic analysis of poisoning risks in LLM-based prompt optimization.<n>We find systems are substantially more vulnerable to manipulated feedback than to injected queries.<n>We propose a lightweight highlighting defense that reduces the fake-reward $Delta$ASR from 0.23 to 0.07 without degrading utility.
  arXiv  Detail & Related papers  (2025-10-16T07:28:54Z)
• Active Attacks: Red-teaming LLMs via Adaptive Environments [71.55110023234376]
  We address the challenge of generating diverse attack prompts for large language models (LLMs)<n>We introduce textitActive Attacks, a novel RL-based red-teaming algorithm that adapts its attacks as the victim evolves.
  arXiv  Detail & Related papers  (2025-09-26T06:27:00Z)
• Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking [44.8238758047607]
  Existing adversarial attacks typically target harmful responses in single-point, greedy generations.<n>We propose a novel framework for adversarial evaluation that explicitly models the entire output distribution, including tail-risks.<n>Our framework also enables us to analyze how different attack algorithms affect output harm distributions.
  arXiv  Detail & Related papers  (2025-07-06T16:13:33Z)
• The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems [101.68501850486179]
  We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities.<n>This task aims to find imperceptible perturbations that retrieve a target document, originally excluded from the initial top-$k$ candidate set.<n>We propose ReGENT, a reinforcement learning-based framework that tracks interactions between the attacker and the target RAG.
  arXiv  Detail & Related papers  (2025-05-24T08:19:25Z)
• AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models [0.0]
  Large Language Models (LLMs) continue to exhibit vulnerabilities to jailbreaking attacks.<n>We present AutoAdv, a novel framework that automates adversarial prompt generation.<n>We show that our attacks achieve jailbreak success rates of up to 86% for harmful content generation.
  arXiv  Detail & Related papers  (2025-04-18T08:38:56Z)
• REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective [57.57786477441956]
  We propose an adaptive and semantic optimization problem over the population of responses.<n>Our objective doubles the attack success rate (ASR) on Llama3 and increases the ASR from 2% to 50% with circuit breaker defense.
  arXiv  Detail & Related papers  (2025-02-24T15:34:48Z)
• Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks [12.061098193438022]
  Retrieval Augmented Generation (RAG) is a technique commonly used to equip models with out of distribution knowledge. This paper investigates the security of RAG systems against end-to-end indirect prompt manipulations.
  arXiv  Detail & Related papers  (2024-08-09T12:26:05Z)
• Rethinking Targeted Adversarial Attacks For Neural Machine Translation [56.10484905098989]
  This paper presents a new setting for NMT targeted adversarial attacks that could lead to reliable attacking results. Under the new setting, it then proposes a Targeted Word Gradient adversarial Attack (TWGA) method to craft adversarial examples. Experimental results demonstrate that our proposed setting could provide faithful attacking results for targeted adversarial attacks on NMT systems.
  arXiv  Detail & Related papers  (2024-07-07T10:16:06Z)
• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models.<n>We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks.<n>We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• Automatic and Universal Prompt Injection Attacks against Large Language Models [38.694912482525446]
  Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. These attacks manipulate applications into producing responses aligned with the attacker's injected content, deviating from the user's actual requests. We introduce a unified framework for understanding the objectives of prompt injection attacks and present an automated gradient-based method for generating highly effective and universal prompt injection data.
  arXiv  Detail & Related papers  (2024-03-07T23:46:20Z)
• DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models [64.79319733514266]
  Adversarial attacks can introduce subtle perturbations to input data. Recent attack methods can achieve a relatively high attack success rate (ASR) We propose a Distribution-Aware LoRA-based Adversarial Attack (DALA) method.
  arXiv  Detail & Related papers  (2023-11-14T23:43:47Z)
• Attack Prompt Generation for Red Teaming and Defending Large Language Models [70.157691818224]
  Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. We propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts.
  arXiv  Detail & Related papers  (2023-10-19T06:15:05Z)
• Generalizable Black-Box Adversarial Attack with Meta Learning [54.196613395045595]
  In black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful perturbation based on query feedback under a query budget. We propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance.
  arXiv  Detail & Related papers  (2023-01-01T07:24:12Z)

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.