REVIEW 7 cited by
SPML: A DSL for Defending Language Models Against Prompt Attacks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Large language models (LLMs) have profoundly transformed natural language applications, with a growing reliance on instruction-based definitions for designing chatbots. However, post-deployment the chatbot definitions are fixed and are vulnerable to attacks by malicious users, emphasizing the need to prevent unethical applications and financial losses. Existing studies explore user prompts' impact on LLM-based chatbots, yet practical methods to contain attacks on application-specific chatbots remain unexplored. This paper presents System Prompt Meta Language (SPML), a domain-specific language for refining prompts and monitoring the inputs to the LLM-based chatbots. SPML actively checks attack prompts, ensuring user inputs align with chatbot definitions to prevent malicious execution on the LLM backbone, optimizing costs. It also streamlines chatbot definition crafting with programming language capabilities, overcoming natural language design challenges. Additionally, we introduce a groundbreaking benchmark with 1.8k system prompts and 20k user inputs, offering the inaugural language and benchmark for chatbot definition evaluation. Experiments across datasets demonstrate SPML's proficiency in understanding attacker prompts, surpassing models like GPT-4, GPT-3.5, and LLAMA. Our data and codes are publicly available at: https://prompt-compiler.github.io/SPML/.
Forward citations
Cited by 7 Pith papers
-
What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs
Many-shot jailbreak success in several LLMs depends mainly on total context length, not on whether the in-context examples are harmful, safe, or meaningless.
-
SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
A systemization of LLM jailbreak security that adds linked taxonomies, an evaluation platform, and JailbreakDB, while its main attack–defense comparison results remain deferred.
-
The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense
Near-perfect jailbreak defenses for vision-language models are mostly over-refusal, and the two standard ways of scoring jailbreaks agree only at chance level.
-
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.
-
Lightweight Safety Classification Using Pruned Language Models
Intermediate-layer features of small LLMs plus a penalized logistic regression classifier achieve high F1 scores on content safety and prompt injection classification with very few labeled examples, per the paper's ex...
-
Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents
A survey proposing a source-and-impact taxonomy (input, model, combined; security, privacy, ethics) for threats to LLM-based agents, with feature analysis and four case studies.
-
Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective
A structured survey of jailbreak prompts and layered defenses for large language models, with six illustrative case studies and no empirical evaluation.
Discussion (0). Continue with ORCID to comment.