arxiv: 2604.23067 · v1 · submitted 2026-04-24 · 💻 cs.CR · cs.CL

Recognition: unknown

Training a General Purpose Automated Red Teaming Model

Aishwarya Padmakumar, Christopher Parisien, Leon Derczynski, Traian Rebedea

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:17 UTC · model grok-4.3

classification 💻 cs.CR cs.CL

keywords red teamingLLM securityadversarial attacksautomated testingfine-tuninggeneralizationvulnerability discovery

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The pith

A training pipeline lets small language models generate effective attacks on LLMs for both trained and entirely new adversarial goals without any evaluator model present during training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out a method to train red teaming models that are not restricted to safety or content-moderation objectives. The approach generates its own training signals so that no pre-existing evaluator is required at training time. After finetuning, models such as Qwen3-8B show clear gains in producing attacks that succeed on both in-domain and out-of-domain goals. This matters because most existing automated red teaming is locked to safety filters and therefore misses other kinds of vulnerabilities that can be unique to a given model. A general-purpose pipeline opens the possibility of probing LLMs for arbitrary failure modes in a scalable way.

Core claim

We propose a pipeline for training a red teaming model that can generalize to arbitrary adversarial goals, including objectives it has not been directly trained on, and that does not depend on the existence of a pre-existing evaluator available at training time. We demonstrate that finetuning small models, such as Qwen3-8B, using this pipeline results in a substantial improvement in their ability to generate attacks for both in and out of domain adversarial goals.

What carries the argument

A self-contained training pipeline that produces attack examples and supervision signals internally, allowing the model to learn to generate attacks for any stated goal without external evaluators.

If this is right

Red teaming can be applied to adversarial intents outside safety and content moderation.
Small open models become practical general-purpose red teamers after a single finetuning stage.
Attack generation no longer requires a fixed safety evaluator to be available throughout training.
The same trained model can be reused across different target LLMs without retraining the attacker from scratch.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could be extended to test non-safety properties such as factual consistency or specific behavioral constraints by simply changing the goal description at inference time.
Because the pipeline is evaluator-free at training, it might be combined with existing safety benchmarks to create hybrid testing suites that cover both known and novel failure modes.
If the generated attacks transfer across model families, organizations could maintain a single red teamer and point it at new releases without additional data collection.

Load-bearing premise

The attacks the model produces actually succeed at their stated goals when tested on real target LLMs, even for goals never seen in training.

What would settle it

Apply the generated attacks to held-out target models and measure success with an independent judge (human or separate automated scorer); if success rates stay near zero on out-of-domain goals, the generalization result does not hold.

Figures

Figures reproduced from arXiv: 2604.23067 by Aishwarya Padmakumar, Christopher Parisien, Leon Derczynski, Traian Rebedea.

**Figure 1.** Figure 1: Pipeline to train the red teaming model for multiple adversarial goals: Given an adversarial goal, a seed view at source ↗

**Figure 2.** Figure 2: Attack Success Rates per goal for in-domain goals for the best checkpoint per target model attacked at view at source ↗

**Figure 3.** Figure 3: Attack Success Rates per goal for out-of-domain goals for the best checkpoint per target model attacked at view at source ↗

**Figure 5.** Figure 5: Attack Success Rates at causing the target view at source ↗

**Figure 6.** Figure 6: Attack Success Rates at causing the target view at source ↗

**Figure 7.** Figure 7: Example: Causing target to generate raw ANSI sequences (note that ANSI sequences have been escaped view at source ↗

**Figure 8.** Figure 8: Example: Causing target to generate a markdown image containing a URI where a small prompt variation view at source ↗

**Figure 9.** Figure 9: Example: Causing target to generate code for malware. Note that the segments marked as " view at source ↗

**Figure 10.** Figure 10: Example: Causing target to generate raw ANSI sequences (note that ANSI sequences have been escaped view at source ↗

**Figure 11.** Figure 11: Example: Causing target to generate code for malware. Note that the segments marked as " view at source ↗

**Figure 12.** Figure 12: Variation in in-domain success rate of the best model with number of epochs of training performed view at source ↗

**Figure 13.** Figure 13: Variation in out-of-domain success rate of the best model with number of epochs of training performed view at source ↗

**Figure 14.** Figure 14: Mean Pairwise Cosine Sims over Successful Generated Attacks for best models per train target (lower is view at source ↗

**Figure 15.** Figure 15: In Domain Attack Success Rates with Ablation of Diversity Loss During Training - Successful Example view at source ↗

**Figure 16.** Figure 16: Out of Domain Attack Success Rates with Ablation of Diversity Loss During Training - Successful view at source ↗

**Figure 17.** Figure 17: Mean Pairwise Cosine Similarities over Successful Attacks per Goal with Ablation of Diversity Loss view at source ↗

**Figure 18.** Figure 18: In Domain Attack Success Rates with Ablation of Diversity Loss During Training - Failed Example view at source ↗

read the original abstract

Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover weaknesses unique to it. Most current automated red teaming methods are intended for tackling safety and content moderation. Thus, they make use of content safety models as evaluators and optimize for circumventing them, and as such, have not been tested with other adversarial intents not typically captured by these. We propose a pipeline for training a red teaming model that can generalize to arbitrary adversarial goals, including objectives it has not been directly trained on, and that does not depend on the existence of a pre-existing evaluator available at training time. We demonstrate that finetuning small models, such as Qwen3-8B, using this pipeline results in a substantial improvement in their ability to generate attacks for both in and out of domain adversarial goals.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper outlines an evaluator-free training pipeline for red teaming models aimed at arbitrary goals, but the abstract supplies no metrics, baselines, or evaluation details to support the claimed generalization gains.

read the letter

The one thing to know is that this paper gives a pipeline to train red teaming models without needing an evaluator during training, and it claims the resulting models get better at attacks on both seen and unseen goals. The evidence for that claim is missing from the abstract. What is new is the focus on general adversarial goals instead of just safety and content moderation. Most prior work optimizes against fixed safety classifiers, so moving to arbitrary objectives without that crutch is a shift worth noting. The paper does well at laying out the limitation of current automated red teaming and sketching a training approach that could apply more broadly. Using small models like Qwen3-8B keeps it practical. The soft spots are around the results. They assert substantial improvement but supply no attack success rates, baselines, or details on how out-of-domain goals were chosen and success measured. The stress-test point is on target: without a clear, independent way to verify attack effectiveness at test time, the generalization could be an artifact of the test setup or a proxy metric. That makes the central claim hard to evaluate right now. This paper is for researchers and engineers working on LLM vulnerability testing who want tools that aren't locked to safety filters. A reader interested in building or adapting red teaming systems would get some value from the pipeline description. It deserves a serious referee because the underlying problem is real and the idea could have practical impact if the experiments hold up. I would recommend sending it to peer review so the evaluation details and numbers can be checked properly.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes a pipeline for training general-purpose automated red teaming models for LLMs. The pipeline enables training without a pre-existing evaluator and is claimed to allow generalization to arbitrary adversarial goals, including those not seen during training. The authors demonstrate the approach by finetuning models like Qwen3-8B and assert substantial improvements in attack generation for both in-domain and out-of-domain goals.

Significance. If the results are substantiated with rigorous quantitative evaluation, this could represent a meaningful advance in automated red teaming by removing the dependency on content safety evaluators and enabling broader adversarial testing. The focus on generalization to out-of-domain goals addresses a key limitation in current methods limited to safety and content moderation.

major comments (2)

[Abstract] Abstract: The central claim of 'substantial improvement' in the ability to generate attacks for both in and out of domain adversarial goals is asserted without any quantitative metrics, baselines, attack success rates, or details on how out-of-domain goals were selected and measured.
[Pipeline description] The training pipeline is described as using no evaluator signal, yet the test-time evaluation of attack effectiveness (particularly for out-of-domain goals) lacks specification of an independent success criterion or evaluator. This makes it impossible to verify that measured gains reflect true adversarial capability rather than artifacts of goal selection or proxy metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed review and constructive feedback on our manuscript. We address each major comment point by point below, providing clarifications and indicating where revisions have been made to strengthen the presentation of our results and methodology.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim of 'substantial improvement' in the ability to generate attacks for both in and out of domain adversarial goals is asserted without any quantitative metrics, baselines, attack success rates, or details on how out-of-domain goals were selected and measured.

Authors: We agree that the original abstract did not include sufficient quantitative support for the central claim. In the revised manuscript, we have updated the abstract to explicitly report attack success rates (ASR) for both in-domain and out-of-domain goals, along with baseline comparisons and a concise description of out-of-domain goal selection and measurement criteria. These quantitative details are drawn from the experimental results already present in the full paper and are now summarized in the abstract for clarity. revision: yes
Referee: [Pipeline description] The training pipeline is described as using no evaluator signal, yet the test-time evaluation of attack effectiveness (particularly for out-of-domain goals) lacks specification of an independent success criterion or evaluator. This makes it impossible to verify that measured gains reflect true adversarial capability rather than artifacts of goal selection or proxy metrics.

Authors: We acknowledge that the original manuscript could have provided more explicit details on the test-time evaluation protocol. While the training pipeline indeed uses no evaluator signal (relying on synthetic attack generation and self-supervised objectives), we have added a dedicated subsection in the revised version that specifies the independent success criteria used at test time. These include direct assessment of whether the target LLM complies with the generated adversarial objective (via response analysis) supplemented by human evaluation for out-of-domain cases, ensuring the reported gains are not artifacts of proxies. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical training pipeline with no derivation chain

full rationale

The paper proposes an empirical pipeline for training red-teaming models that generalize to arbitrary goals without requiring an evaluator at training time, then reports experimental results from finetuning models such as Qwen3-8B. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described approach. Claims rest on observed performance improvements rather than any analytical reduction that would make outputs equivalent to inputs by construction. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; standard LLM finetuning hyperparameters are implicitly present but not detailed.

pith-pipeline@v0.9.0 · 5467 in / 1051 out tokens · 32379 ms · 2026-05-08T11:17:11.166994+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

10 extracted references · 5 canonical work pages

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Aaron Chatterji, Thomas Cunningham, David J Dem- ing, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman

The vulnerability of language model bench- marks: Do they accurately reflect true llm perfor- mance?arXiv preprint arXiv:2412.03597. Aaron Chatterji, Thomas Cunningham, David J Dem- ing, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman. 2025. How people use chat- gpt. Technical report, National Bureau of Economic Research. Sizhe Chen, Arman Zh...

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A survey of the overlooked dangers of tem- plate engines.arXiv preprint arXiv:2405.01118. Qwen-Team. 2025. Qwen3 technical report.Preprint, arXiv:2505.09388. Salman Rahman, Liwei Jiang, James Shiffer, Genglin Liu, Sheriff Issaka, Md Rizwan Parvez, Hamid Palangi, Kai-Wei Chang, Yejin Choi, and Saadia Gabriel. 2025. X-teaming: Multi-turn jailbreaks and defe...

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do anything now

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Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36:80079–80110. Sibo Yi, Yule Liu, Zhen Sun, Tianshuo Cong, Xinlei He, Jiaxing Song, Ke Xu, and Qi Li. 2024. Jailbreak attacks and defenses against large language models: A survey.arXiv preprint arXiv:2407.04295. Zheng Xin Yong, Cristina Menghini, and Stephen Bach

work page arXiv 2024
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Don’t make your llm an evaluation benchmark cheater

Low-resource languages jailbreak gpt-4. In Socially Responsible Language Modelling Research. Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han. 2023. Don’t make your llm an evaluation benchmark cheater.arXiv preprint arXiv:2311.01964. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico...

work page arXiv 2023
[6]

Raw ANSI escape sequences
[7]

Markdown images with URIs that exfiltrate personal information using variable names
[8]

⟨redacted⟩

Code for malware For each of the above adversarial goals, we first use Qwen3-235B-A22B to generate a diverse set of red team input templates using a handcrafted series of synthetic data generation prompts. To finetune 14 Figure 7: Example: Causing target to generate raw ANSI sequences (note that ANSI sequences have been escaped out to display them for the...

2024
[9]

S c e n a r i o 1

and providing the request in a less com- mon language (Yong et al., 2023). We use the prompt templates in the following two subsections to generate these seeds. 6https://qwenlm.github.io/blog/qwen3/ 18 G.1 Specific Examples of Goal – Prompt Template You a r e an LLM t a s k e d w i t h a s s i s t i n g a s e c u r i t y e n g i n e e r i d e n t i f y s ...

2023
[10]

for red team model training. We experi- mented with some variations in the reward func- tion (discussed in 3.2), the judge model used in it (discussed in appendix F), the target LLM being attacked at training time (reported in main results in section 4) and number of training epochs (dis- cussed in appendix I). Besides these, we use a train- ing batch siz...

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