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garak: A Framework for Security Probing Large Language Models
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As Large Language Models (LLMs) are deployed and integrated into thousands of applications, the need for scalable evaluation of how models respond to adversarial attacks grows rapidly. However, LLM security is a moving target: models produce unpredictable output, are constantly updated, and the potential adversary is highly diverse: anyone with access to the internet and a decent command of natural language. Further, what constitutes a security weak in one context may not be an issue in a different context; one-fits-all guardrails remain theoretical. In this paper, we argue that it is time to rethink what constitutes ``LLM security'', and pursue a holistic approach to LLM security evaluation, where exploration and discovery of issues are central. To this end, this paper introduces garak (Generative AI Red-teaming and Assessment Kit), a framework which can be used to discover and identify vulnerabilities in a target LLM or dialog system. garak probes an LLM in a structured fashion to discover potential vulnerabilities. The outputs of the framework describe a target model's weaknesses, contribute to an informed discussion of what composes vulnerabilities in unique contexts, and can inform alignment and policy discussions for LLM deployment.
Forward citations
Cited by 9 Pith papers
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Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection
The work introduces and partially evaluates seven cross-domain prompt injection detectors, reporting F1 gains on benchmarks like deepset/prompt-injections and indirect-injection sets via local alignment, stylometry, a...
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AMT-X: Phase-Structured Multi-Turn Red-Teaming with Checklist-Gated Evaluation
A phase-structured multi-turn red-team framework reports 97.6–100% lenient ASR but only 66.7–78.6% full actionable ASR on six frontier LLMs, with success strongly depth-dependent.
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.
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Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models
Reasoning-augmented LLMs are on average about 3 points more robust to prompt attacks, but category-level results flip this, including a 32-point higher success rate for tree-of-attacks jailbreaks.
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A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.
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OneShield -- the Next Generation of LLM Guardrails
A paper describes OneShield, a model-agnostic guardrail framework with parallel risk detectors and a policy manager, and reports its enterprise deployment and use in InstructLab.
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Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications
A practical framework for application-level LLM safety testing: organization-specific taxonomies plus black-box adversarial evaluation, illustrated by a Singapore government pilot.
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JavelinGuard: Low-Cost Transformer Architectures for LLM Security
A study of five small transformer classifier architectures for LLM jailbreak and prompt injection detection claims low-latency accuracy comparable to large models, led by the multi-task Raudra design.
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LLM Harms: A Taxonomy and Discussion
This paper proposes a taxonomy of LLM harms in five categories and suggests mitigation strategies plus a dynamic auditing system for responsible development.
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