2025/2026 Potential Research Directions in LLM Red Teaming

If you are interested in any of the following direction for your dissertation, feel free to drop me an email or find more at the dissertation portal on blackboard. I supervise both UG and PG.

1. Automated Red Teaming and Adversarial Prompt Generation

Developing LLM-driven or reinforcement learning-based systems to automatically generate diverse and effective adversarial prompts that elicit harmful or unsafe outputs from target models¹.
Exploring black-box and curiosity-driven exploration strategies to maximize coverage and discover novel vulnerabilities, such as mode collapse in RL-based attacks or semantic diversity in prompts¹.
Leveraging GFlowNet fine-tuning, smoothing phases, or deep adversarial interaction frameworks (e.g., DART) for iterative, scalable vulnerability discovery¹².

2. Multimodal Red Teaming

Extending red teaming beyond text to vision-language models (VLMs), testing for vulnerabilities when LLMs process images, audio, or other modalities².
Creating specialized datasets and evaluation frameworks for multimodal faithfulness, privacy, safety, and fairness².

3. Domain-Specific and Contextual Red Teaming

Designing red teaming exercises tailored to sensitive domains such as healthcare, law, or finance, where LLM outputs can have high-stakes consequences (e.g., clinical decision support, legal advice)².
Incorporating domain experts (e.g., child safety, suicide prevention, hate speech specialists) to craft contextually relevant and culturally nuanced attack prompts, including in multiple languages and dialects³².

4. Jailbreaking and Prompt Injection Research

Systematically cataloging and evolving jailbreak techniques (e.g., reframing requests, multi-turn attacks, or exploiting system message weaknesses) to bypass safety guardrails⁴¹.
Investigating the effectiveness of prompt injection attacks in various deployment contexts, including retrieval-augmented generation (RAG) systems and autonomous agents⁴⁵.

5. Privacy and Data Leakage Testing

Probing for unintentional leakage of personally identifiable information (PII) or proprietary data, especially in models trained on mixed or sensitive datasets⁴².
Red teaming for indirect leakage via model memorization, output formatting, or inference-time vulnerabilities⁴.

6. Bias, Fairness, and Stereotype Propagation

Evaluating LLM responses for subtle and overt biases, discrimination, or stereotype reinforcement, particularly across different demographic groups or under adversarial framing⁴².
Building comprehensive red teaming datasets and metrics for fairness evaluation, including intersectional and cross-lingual scenarios³².

7. Robustness and Consistency Under Adversarial Perturbations

Testing LLMs’ ability to maintain safety, factuality, and formatting under slight prompt changes, paraphrasing, or adversarial context switching⁴¹.
Developing fuzzing-inspired frameworks (e.g., GPTFuzzer) to automate the discovery of robustness failures².

8. Realistic Attack Simulations and Adversary Emulation

Emulating advanced persistent threats (APTs) and real-world attacker tactics, such as RAG poisoning, lateral movement, and privilege escalation within LLM-powered systems⁵.
Conducting full-simulation and assumed-breach exercises to test end-to-end system resilience, including infrastructure and access control vulnerabilities⁵.

9. Scaling and Standardizing Red Teaming Methodologies

Researching how to scale red teaming efforts using automation, crowdsourcing, and diverse expert involvement while maintaining quality and coverage³⁶⁷.
Developing standardized benchmarks, reporting protocols, and best practices for LLM red teaming across organizations and regulatory environments⁷⁵.

10. Human Factors and Team Diversity in Red Teaming

Studying the impact of team composition—diversity in expertise, background, and lived experience—on the effectiveness and creativity of red teaming outcomes³⁸.
Investigating collaborative and “alchemist mindset” approaches for boundary-pushing, playful, and creative exploration of LLM vulnerabilities⁷⁸.

Summary Table of Research Directions

Research Direction	Key Focus Areas
Automated Red Teaming	RL-based prompt generation, black-box attacks, curiosity-driven exploration
Multimodal Red Teaming	Vision-language models, multimodal privacy/safety/fairness datasets
Domain-Specific Red Teaming	Healthcare, law, finance, multi-lingual and cultural context
Jailbreaking \& Prompt Injection	Cataloging jailbreaks, prompt injection in RAG and agents
Privacy/Data Leakage	PII leakage, memorization, output formatting vulnerabilities
Bias \& Fairness	Stereotype propagation, intersectional/cross-lingual bias testing
Robustness Testing	Fuzzing, adversarial perturbations, consistency under paraphrasing
Realistic Attack Simulation	APT emulation, full-simulation, assumed-breach, infrastructure vulnerabilities
Scaling/Standardization	Automation, crowdsourcing, best practices, regulatory compliance
Human Factors	Team diversity, creativity, collaborative exploration

These research directions collectively address the evolving landscape of LLM vulnerabilities, emphasizing both technical sophistication and the importance of human creativity and diversity in red teaming efforts⁴³¹²⁸.

https://arxiv.org/html/2410.09097v2 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶
https://coralogix.com/ai-blog/red-teaming-for-large-language-models-a-comprehensive-guide/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰
https://www.ofcom.org.uk/siteassets/resources/documents/consultations/discussion-papers/red-teaming/red-teaming-for-gen-ai-harms.pdf?v=370762 ↩ ↩² ↩³ ↩⁴ ↩⁵
https://www.confident-ai.com/blog/red-teaming-llms-a-step-by-step-guide ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷
https://www.vktr.com/digital-workplace/the-enterprise-playbook-for-llm-red-teaming/ ↩ ↩² ↩³ ↩⁴
https://hdsr.mitpress.mit.edu/pub/ded4vcwl ↩
https://developer.nvidia.com/blog/defining-llm-red-teaming/ ↩ ↩² ↩³
https://www.superannotate.com/blog/llm-red-teaming ↩ ↩² ↩³

Lecturer @ShefNLP