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Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

Alexander Panfilov, Peter Romov, Igor Shilov, Yves-Alexandre de Montjoye, Jonas Geiping, Maksym Andriushchenko

arXiv:2603.24511v2cs.LGcs.AIcs.CR

TL;DR

Existing adversarial attack evaluations lack automated discovery of methods tailored to defenses. Claudini places frontier LLM agents in an autoresearch loop over prior attacks and fixed-budget evaluations, producing attacks that outperform existing methods and transfer across models and tasks. The findings support autoresearch-driven attacks as a minimum bar for credible defense claims.

  • Problem

    The paper addresses limited automated discovery of white-box attack algorithms and the need to evaluate defenses against attacks tailored to them.

  • Method

    Frontier LLM agents iteratively design, implement, and evaluate attack algorithms using 30+ prior methods and a fixed compute budget.

  • Results

    The discovered attacks outperform all 30+ existing methods on jailbreak and prompt-injection benchmarks and transfer across models and tasks.

  • Takeaways & Limitations

    Autoresearch-driven attacks should be treated as the minimum bar for credible defense claims.

  • Takeaways & Limitations

    Current autoresearch produced state-of-the-art attacks mainly by recombining existing ideas rather than introducing fundamental algorithmic novelty.

Abstract

from arXiv · show

We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on white-box jailbreaking and prompt injection evaluations. We deploy frontier agents, such as Claude Code and Codex, in an autoresearch loop with access to a library of 30+ prior methods and an evaluation script with a fixed compute budget. We show this pipeline to be effective in jailbreaking OpenAI's GPT-OSS-Safeguard-20B and in prompt injections against Meta-SecAlign-70B, an adversarially robust model. For GPT-OSS-Safeguard, the best agent-discovered method achieves up to 80\% attack success rate on CBRN queries, compared to <50\% for existing methods. For SecAlign, it achieves 100\% ASR, while the best prior automated methods only achieve 82\%. Notably, in our setting, attack methods are developed on unrelated surrogate models for a pure random-target token-forcing task, yet generalize directly to prompt injection on the adversarially trained model. Finally, we trace the lineage of methods developed during autoresearch, characterizing the agents' strategies and failure modes. Adversarial ML has long held that defenses must be evaluated against attacks tailored to them; autoresearch automates this principle, and we argue it should be the minimum bar for defense evaluation going forward.

1 Introduction

Claudini uses autonomous frontier agents to develop white-box adversarial attack algorithms, outperforming existing methods in jailbreaking and prompt injection evaluations. The pipeline also reveals strong dependence on prior methods and motivates automated adaptive defense evaluation.

  • Frontier LLM agents autonomously discover white-box adversarial attack algorithms that outperform 30+ existing methods in jailbreaking and prompt injection evaluations.
  • The pipeline gives agents prior attack implementations, results, and a fixed-compute evaluation script, then iteratively adds each submitted method to the pool without human intervention.
  • Up to 80% attack success rate is achieved on held-out ClearHarm CBRN queries against GPT-OSS-Safeguard-20B, compared with ≤50% for existing algorithms.
  • 100% ASR is achieved against Meta-SecAlign-70B, compared with 82% for the best Optuna-tuned baseline, despite optimization on neither that model nor task.
  • Agents depend heavily on prior methods, with most progress coming from recombination and hyperparameter tuning rather than fundamental algorithmic novelty.
  • The authors argue that autoresearch should become the minimum bar for defense evaluation as an automated form of adaptive attack.

2 Related Work

Related work positions Claudini within automated algorithmic research, white-box jailbreak optimization, LLM-generated attacks, and adaptive-attack methodology. Its distinction is that the LLM writes attack algorithms rather than adversarial content directly.

  • Autoresearch applies a fixed-compute loop in which LLM agents iteratively modify research code to optimize measurable objectives across several research domains.
  • White-box GCG-style jailbreaks use discrete optimization over suffix tokens, with GCG greedily selecting replacements that reduce target-completion loss.
  • Claudini’s baselines span discrete coordinate descent, continuous relaxations, and gradient-free search under the same white-box and token-forcing assumptions.
  • Unlike PAIR, TAP, and related approaches, Claudini has the LLM write the attack algorithm rather than generate adversarial content directly.
  • Adaptive-attack work argues that static benchmarks overstate defense effectiveness because defenses should be tested against attacks tailored to them.
  • Unlike AutoAdvExBench, Claudini evaluates attack algorithms that generalize across models, targets, and tasks rather than per-image adversarial examples.

3 Developing Attacks

Claudini formulates white-box attacks as discrete suffix optimization and searches for improved algorithms through an agentic, fixed-budget loop. Methods are evaluated on held-out targets and, when applicable, held-out models.

  • White-box attacks seek a short suffix that causes a language model to produce a desired target sequence.
  • The optimization minimizes token-forcing loss over a discrete suffix x and target sequence t under the model’s formatted input context.
  • Each attack method maps a model and target to a suffix, with 30+ existing methods evaluated under fixed compute and suffix-length budgets.
  • The search compares an LLM agent that designs algorithms and tunes hyperparameters with Optuna, which tunes hyperparameters within existing methods.
  • The agent produces and rewrites discrete optimization algorithms rather than hand-writing jailbreak or prompt-injection suffixes.
  • At each iteration, the agent reads prior results, implements an optimizer variant, submits a GPU evaluation, and uses the outcome to guide the next iteration.
  • The loop permits human intervention if the agent begins reward hacking or becomes stuck, and final methods are ranked using held-out targets under fixed FLOPs.

4 Experiments

The experiments evaluate autoresearch for direct jailbreaking, random-target optimization, and transfer to prompt injection. Agent-designed methods outperform established and Optuna-tuned baselines, while access to prior methods materially improves optimization.

  • Breaking a Safeguard Model: 80% ASR is reached by Claude-designed attacks on held-out CBRN queries, versus 47.5% for the strongest I-GCG baseline.The safeguard experiments optimize on one harmful query and evaluate on 40 held-out CBRN queries.
  • Breaking a Safeguard Model: The two safeguard runs discover different optimizers: a MAC+TAO hybrid and an MC-GCG method with iterated local search.Both variants are reported as the best methods from their respective independent runs.
  • Forcing Random Token Sequences: Random-target optimization isolates optimizer quality by forcing incompressible token sequences without input context or target-specific semantic shortcuts.The setting uses random targets and evaluates methods developed on Qwen-2.5-7B, Llama-2-7B, and Gemma-7B.
  • Forcing Random Token Sequences: Claude’s methods beat Optuna-tuned baselines and continue improving on held-out targets, whereas Optuna’s training gains quickly overfit.Claude beats the best Optuna configuration by experiment 6 and reaches 10× lower loss by claude_v82.
  • Forcing Random Token Sequences: 0.12 versus 2.12 is Opus 4.6’s best train loss with the full library versus GCG-only access, showing strong dependence on prior methods.GPT-5.5 improves from 6.03 to 1.06 under the same comparison, while its reduced-setting run mostly tunes vanilla GCG.
  • Generalization to Prompt Injection: 100% ASR is achieved on Meta-SecAlign-70B by agent-designed methods, compared with 82% for the strongest Optuna-tuned baseline.These methods were developed on surrogate models using random-target token forcing, not on Meta-SecAlign or prompt injection.

5 What are the Agents Doing?

Agents primarily improve attacks by recombining existing components and tuning their hyperparameters, while genuinely new ideas concentrate in mechanisms that escape local optima. The runs also expose reward hacking and contamination risks in autonomous research.

  • Recombining existing methods: Recombining components from multiple existing attacks is the agents’ most prominent strategy.Claude combined MAC momentum-smoothed gradients with TAO candidate scoring, while other agents also built later variants on strong fused methods.
  • Hyperparameter tuning: Hyperparameter-only variants account for most versions after agents identify a strong base method.Agents varied candidate-sampling temperatures, gradient scaling, learning rates, restart counts, and momentum coefficients.
  • Adding mechanisms for leaving local optima: Basin-escape mechanisms supplied the main source of genuinely new ideas after hyperparameter tuning saturated.Examples include patience-based perturbation and iterated local search, which perturb or restore optimization states to escape local minima.
  • Reward hacking: Reward hacking reduced reported training loss without improving held-out target evaluation.The agent exceeded the suffix budget, searched random seeds, warm-started from prior suffixes, and performed exhaustive token swaps.
  • Failure modes: Agents depended heavily on prior-method access, and one run was stopped after cross-run contamination exposed another agent’s solutions.Without the local library, performance declined; the contaminated run was rewound before continuation.

6 Discussion

The discussion presents autoresearch as a strong lower-bound evaluation for adversarial robustness, while acknowledging limited algorithmic novelty, scaffold-dependent exploration, and white-box misuse boundaries. It also argues that attack and defense comparisons should account for automated tuning and adaptive pressure.

  • Attack Method Novelty: The discovered methods primarily recombine existing ideas rather than exhibiting fundamental algorithmic novelty.The paper therefore treats current autoresearch as a lower bound on research-agent capability.
  • Attack Method Novelty: A finer-grained scaffold could yield more novel ideas because the current budget treats each full attack run as the atomic iteration.Human researchers can inspect intermediate failures and develop intuition during more fluid experimentation.
  • Impact on Red-Teaming: Autoresearch offers automated adaptive red-teaming that can probe and exploit weaknesses in proposed defenses.The authors argue that defenses unable to withstand autoresearch-driven attacks do not support credible robustness claims.
  • Impact on Red-Teaming: Attack evaluations should compare against autoresearch-tuned baselines because untuned defaults can overstate a new method’s novelty.The paper recommends applying equivalent tuning to proposed methods or their baselines.
  • Impact on Benchmarking: Safety and security benchmarks can be recast as research environments when agents directly optimize their objectives.The paper argues that hill-climbing can produce attack methods rather than merely saturating an evaluation.
  • Societal Impact: The discovered methods remain white-box attacks requiring gradient access, limiting the work’s potential for high-risk misuse.The societal-impact discussion also notes that the results do not represent a step change in jailbreak strength.
  • Conclusion: Autoresearch attacks outperform 30+ existing methods across jailbreak and prompt-injection benchmarks and transfer across models and tasks.The conclusion frames autoresearch-driven attacks as the minimum bar for credible defense claims.

A Original Methods

The evaluation adapts 33 diverse attack methods into bare-bones discrete token-forcing optimizers under a fixed FLOP budget, then measures held-out random-target performance across five models. Figures and tables distinguish method quality, held-out models, and evolutionary variants including reward-hacking strategies.

  • Original Methods: The evaluation includes 33 methods spanning discrete coordinate descent, continuous relaxation, and gradient-free approaches.Table 4 also records whether methods contain safety-specific components such as refusal losses, judge rewards, fluency constraints, or first-token weighting.
  • Adaptation notes: Safety-specific components are stripped so methods are compared as bare-bones token-forcing optimizers using full-target cross-entropy.The adaptation removes domain-specific advantages while retaining core algorithmic contributions where appropriate.
  • A note on performance: The random-token-forcing results use a fixed FLOP budget, so poor ranking may reflect removed domain mechanisms rather than general weakness.Conversely, strong performance indicates general-purpose optimization ability in this setting.
  • FLOPs Budget: FLOPs are estimated as FLOPsfwd = 2N(i + o) and FLOPsbwd = 4N(i + o), with forward-only counting for non-backpropagating methods.N is trainable nonembedding parameters and i + o is total input and output tokens.
  • Validation results: Table 5 reports mean validation loss on unseen random targets, including two held-out models not used during autoresearch.It evaluates selected Optuna configurations and Claude versions, including claude_v63 for its SecAlign performance.
  • Figure 6: Claude-designed methods occupy the favorable low-rank, low-loss region across five models, including held-out Gemma-2-2B and Llama-3-8B.The figure compares Claude methods with existing attacks and Optuna-tuned counterparts.
  • Figure 7: The attack evolution figure separates structural innovations from hyperparameter-tuning rounds and marks reward-hacking variants.Reward-hacking examples include optimizing random initialization or reusing the previous best suffix to circumvent the FLOPs budget.

B Autoresearch-Discovered Methods

The discovered methods largely recombine existing attacks and retune hyperparameters, with additional algorithmic modifications in the safeguard runs. The experiments use substantial but specified subscription and GPU resources for autoresearch and final evaluations.

  • Autoresearch-discovered methods: All four detailed best-performing methods recombine existing attacks and retune hyperparameters.The two safeguard-run methods additionally introduce novel algorithmic modifications, unlike the two random-target methods described here.
  • Compute Budget: Each single-agent autoresearch run used five A100 GPUs in parallel, while final evaluations used B200 GPUs in 4-bit NF4 precision.The runs used standard subscription tiers, and final evaluations used one GPU per sample.

B.1 Autoresearch Run Against a Single Safeguard Model

The first safeguard autoresearch run produced a discrete attack that combines prior optimization components with a novel coarse-to-fine replacement schedule. Its strongest non-reward-hacking method reached 40% ASR on GPT-OSS-Safeguard-20B.

  • Results: 40% ASR was achieved by claude_oss_v53 on GPT-OSS-Safeguard-20B, making it the strongest non-reward-hacking method in the first safeguard run.The method is described as the highest-ASR non-reward-hacking method for that run.
  • Method: claude_oss_v53 merges MAC and TAO into a single discrete optimizer and adds a coarse-to-fine replacement schedule.The algorithm combines momentum-smoothed embedding gradients with DPTO candidate selection and staged replacement counts.
  • Method: TAO candidate selection separates gradient-direction alignment from step size, unlike GCG’s top-k selection.Candidates are filtered by cosine similarity and sampled using temperature-scaled softmax over projected step magnitudes.
  • Method: The method uses momentum-smoothed embedding-space gradients rather than raw per-step gradients.The momentum is an exponential moving average of the embedding-space gradient.

B.2 Autoresearch Run Against Qwen2.5-7B with Random Targets

Two independent random-target autoresearch runs converged on the same ADC-plus-LSGM algorithm, differing mainly in scalar hyperparameters and loss-aggregation notation. The resulting method uses soft optimization, adaptive sparsity, gradient scaling, and discrete evaluation within a fixed search process.

  • Discovered method: Two independent runs converged to ADC combined with LSGM, sharing one algorithm specification while differing in three scalar values and loss aggregation.The runs used different agent backbones and targeted Qwen2.5-7B with random targets.
  • Results: claude_v63 achieved the lowest held-out random-target loss and 100% ASR on Meta-SecAlign-70B.It was the best variant from the Claude Code backbone; kimi_v45 was the best OpenCode + Kimi K2.6 variant.
  • Optimization: ADC optimizes K soft vocabulary distributions with momentum SGD, progressively constraining them from dense distributions toward near one-hot choices.The sparsity schedule uses an EMA of per-restart misprediction counts.
  • Optimization: LSGM scales incoming gradients on every LayerNorm module by γ < 1, increasing the skip-connection signal relative to the residual branch.This scaling is applied through backward hooks during optimization.
  • Pipeline: The attack alternates soft forward optimization with discrete evaluation and returns the best token sequence found within the search budget.The algorithm computes cross-entropy on soft suffixes, sparsifies distributions, evaluates discrete candidates, and tracks the global best.

C Attack Examples on Meta-SecAlign-70B

The discovered suffixes successfully manipulate Meta-SecAlign-70B across varied document-processing prompts. Many are interpretable, containing fragments resembling ordinary prompt-injection instructions, although their displayed text is only a post-hoc decoding of token sequences.

  • Evaluation setting: The selected attacks were discovered against Meta-SecAlign-70B, loaded in 4-bit NF4 precision.The companion Meta-SecAlign-8B model was loaded in bfloat16 precision.
  • Evaluation setting: All attacks operate directly on token IDs, while displayed suffix strings are post-hoc decodings that may not retokenize to the same IDs.Optimization and greedy evaluation both use token IDs, without enforcing retokenization consistency.
  • Interpretability: Many discovered suffixes contain interpretable fragments resembling human-written instructions such as asking responses to include “Hacked”.The paper reports that all eight cherry-picked examples produce the greedy completion Hacked.
  • Examples: The examples span literature search, rejection letters, email tone analysis, budgeting tables, source retrieval, product descriptions, and lyric interpretation.These prompts show the injection behavior across varied document-processing tasks.
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