Adversarial prompts generated against Llama 3.1 8B transfer zero-shot to Llama 3.3 70B

Auto-published from arXiv:2606.24589 by the mine-arxiv pipeline. Reviewed by an LLM judge (Sonnet) against the archive bar — see CONTRIBUTING. Notes: cleared review (confidence 0.78, flags: [no-prompt-excerpt])

Category

jailbreak

Model

Llama 3.3 70B Versatile (Groq)

Surface

API

Setup

AdversaBench pipeline generates adversarial prompt mutations (using five operators: rephrase, inject_distractor, role_flip, constraint_add, jailbreak_wrap) against a weak target (Llama 3.1 8B Instant). The confirmed-failure prompts are then replayed verbatim against Llama 3.3 70B without any re-optimization. The paper reports constraint_add mutations transfer most consistently. No verbatim prompt template is published in the paper; the full dataset is available at https://github.com/khanak0509/AdversaBench.

Observed behavior

A 'substantial fraction' of adversarial prompts that broke Llama 3.1 8B also broke Llama 3.3 70B with zero additional optimization iterations. The paper attributes this to the mutations exploiting general behavioral patterns rather than model-specific weaknesses, with constraint-manipulation operators showing the highest cross-model transfer rate.

Expected behavior

A larger, more capable model should exhibit greater robustness to adversarial mutations optimized against a weaker model, particularly when it has received more alignment training. Cross-model transfer rates should be low if safety training is model-specific and effective.

Reproducibility

medium

Threat model

Adversaries can use cheap, weakly-aligned open-source models (e.g., small Llama variants) as surrogate targets to develop adversarial prompts with low cost, then deploy those prompts against larger production-grade deployments of the same model family. This undermines the safety benefit of scaling alignment efforts on larger models.

Novelty

First systematic confirmation, using a fully automated multi-judge pipeline, that constraint-manipulation adversarial mutations transfer zero-shot across an 8x model size gap within the Llama 3 family, establishing a concrete surrogate-attack threat model for open-weight model families.

Source

• arXiv: 2606.24589
• PDF: https://arxiv.org/pdf/2606.24589
• Categories: cs.AI, cs.CL
• Authors: Khanak Khandelwal (Indian Institute of Technology Jodhpur)

Triage notes (auto)

• paperType: red-team-vuln
• estimatedCaseCount: 3
• triage reason: Automated red-teaming pipeline generating 45 confirmed model-level failures across reasoning, instruction-following, and tool-use via structured mutation operators; reproducible methodology with open code, dataset, and observable behaviors.

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