mlsanityData to 14 Oct 2026

Virtual Context: Enhancing Jailbreak Attacks with Special Token Injection

arXiv 2406.19845 ↗First posted 28 Jun 2024cs.CRcited by 17

On the map: LLM safety and security → Jailbreaking

Abstract (arXiv)

Jailbreak attacks on large language models (LLMs) involve inducing these models to generate harmful content that violates ethics or laws, posing a significant threat to LLM security. Current jailbreak attacks face two main challenges: low success rates due to defensive measures and high resource requirements for crafting specific prompts. This paper introduces Virtual Context, which leverages special tokens, previously overlooked in LLM security, to improve jailbreak attacks. Virtual Context addresses these challenges by significantly increasing the success rates of existing jailbreak methods and requiring minimal background knowledge about the target model, thus enhancing effectiveness in black-box settings without additional overhead. Comprehensive evaluations show that Virtual Context-assisted jailbreak attacks can improve the success rates of four widely used jailbreak methods by approximately 40% across various LLMs. Additionally, applying Virtual Context to original malicious behaviors still achieves a notable jailbreak effect. In summary, our research highlights the potential of special tokens in jailbreak attacks and recommends including this threat in red-teaming testing to comprehensively enhance LLM security.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 21 of its 35 references that are in the map. First posted on arXiv 28 Jun 2024.

Builds on

21 of 35 references.

Closest ancestors · 13

Same direction, newest first

  1. 2024AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens6 Jun
  2. 2024AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs11 Apr
  3. 2024Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks2 Apr
  4. 2024Distract Large Language Models for Automatic Jailbreak Attack13 Mar
  5. 2024Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs21 Feb
  6. 2023DeepInception: Hypnotize Large Language Model to Be Jailbreaker6 Nov
  7. 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
  8. 2023Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation10 Oct
  9. 2023SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks5 Oct
  10. 2023GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts19 Sep
  11. 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
  12. 2023Jailbroken: How Does LLM Safety Training Fail?5 Jul
1 more
  1. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study23 May

Other references · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  7. 2023Safe RLHF: Safe Reinforcement Learning from Human Feedbackcited by 656
  8. 2023RAIN: Your Language Models Can Align Themselves without Finetuningcited by 157

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 6.

17 papers.

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  1. 2025Multilingual and Multi-Accent Jailbreaking of Audio LLMscited by 31
  2. 2025Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expressioncited by 16
  3. 2025E2AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Modelscited by 12
  4. 2025Enhancing Jailbreak Attacks on LLMs via Persona Promptscited by 11
  5. 2024Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerationscited by 8
  6. 2025DP-Fusion: Token-Level Differentially Private Inference for Large Language Modelscited by 6
  7. 2025When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injectioncited by 6
  8. 2025SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defensescited by 5
  9. 2024SQL Injection Jailbreak: A Structural Disaster of Large Language Modelscited by 4
  10. 2025Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessmentcited by 2
  11. 2025The Cost of Thinking: Increased Jailbreak Risk in Large Language Modelscited by 2
  12. 2026Hidden State Poisoning Attacks against Mamba-based Language Modelscited by 2
5 more
  1. 2026Automated jailbreak attack targeting multiple defense strategiescited by 1
  2. 2026GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agentscited by 0
  3. 2026New Wide-Net-Casting Jailbreak Attacks Risk Large Modelscited by 0
  4. 2026Evaluating using Mock Tool Calls to Quarantine Untrusted Prompt Inputscited by 0
  5. 2026Same Bytes, Different Authority: Reserved-Token Representations in Chat-Template Prompt Injectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs
  2. 2024A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models
  3. 2024AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
  4. 2025You Can't Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense
  5. 2025Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
  6. 2023Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks
  7. 2024Characterizing and Evaluating the Reliability of LLMs against Jailbreak Attacks
  8. 2024JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models