---
title: "SIRAJ — paper by Zhou et al. (2026) — summary and ideas"
description: "They test only broad risk categories, and existing red-teaming tools either need white-box access or target narrow threat types. 29 references."
authors: ["Kaiwen Zhou", "Ahmed Elgohary", "A S M Iftekhar", "Amin Saied"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.171/"
doi: "10.18653/v1/2026.findings-eacl.171"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2026-siraj-diverse-efficient-red-teaming/"
---
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# SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning

**Authors:** [Kaiwen Zhou](https://md.research.elamur.ai/free/en/authors/kaiwen-zhou/), [Ahmed Elgohary](https://md.research.elamur.ai/free/en/authors/ahmed-elgohary/), A S M Iftekhar and [Amin Saied](https://md.research.elamur.ai/free/en/authors/amin-saied/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.171/) · [PDF](https://aclanthology.org/2026.findings-eacl.171.pdf) · [DOI 10.18653/v1/2026.findings-eacl.171](https://doi.org/10.18653/v1/2026.findings-eacl.171) · [OpenAlex](https://openalex.org/W7140080041)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLM agents that plan and call tools can cause real-world harm, but current safety evaluations have two gaps. They test only broad risk categories, and existing red-teaming tools either need white-box access or target narrow threat types. SIRAJ is a red-teaming framework that needs only black-box access to the agent. It works in two stages. First, given an agent's tool definitions, it lists fine-grained risk outcomes within each safety category. It then prompts a generator for seed test cases that differ in expected tool-call trajectory and risk source (malicious user or malicious environment). Second, a red-teamer model rewrites each seed into adversarial variants over several rounds, using the agent's execution traces as feedback. To make this cheaper, the authors reformat a large teacher model's reasoning into a concise structured form. They then train Qwen3-8B on successful attacks, first with SFT and then with RL. The distilled 8B model roughly doubles its attack success rate and slightly beats DeepSeek-R1 (671B) while producing far fewer tokens. This makes thorough agent safety testing more affordable.

## Key points
- The seed generation step gives about 2x more diverse tool-calling trajectories and about 2.5x more diverse risk outcomes than baseline prompting.
- Distillation improves the attack success rate (ASR) of Qwen3-8B by more than 100%:
  - +25.8% ASR over the untrained Qwen-8B baseline and +6.0% over R1.
  - RL after SFT adds a further 3.1% average ASR.
- Structured reasoning helps in two ways: it raises the success of training-free red-teamers, and it beats distillation from raw R1 reasoning traces.
- On efficiency, the distilled red-teamer outputs about 3k tokens per seed case, compared with roughly 9–11k for R1 or Structured-R1, using at most K = 3 refinement iterations.
- The model was trained only on GPT-5-mini-based agents, yet it transfers to other agent backbones, including safer ones such as Safe o4-mini and Safe GPT-5-mini.
  - It also improves ASR by more than 20% on agents with unseen tools.
  - However, it still trails R1 on agents whose tools are all unseen.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar and Amin Saied. 2026. SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhou2026siraj,
  title = {{SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning}},
  author = {Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar and Amin Saied},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.171},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.171}
}
```

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*Updated 2 November 2026*