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Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs

Yunhao Chen, Xin Wang, Juncheng Li, Yixu Wang, Jie Li, Yan Teng, Yingchun Wang, Xingjun Ma

arXiv:2511.12710v2cs.CLcs.CR

TL;DR

Automated red teaming often searches prompts or selects strategies instead of optimizing executable attack procedures. EvoSynth evolves code-based attacks through a multi-agent workflow and reports a 95.9% average ASR, outperforming prior methods on Claude-Sonnet-4.5 and GPT-5.

  • Problem

    Many automated red-teaming systems optimize prompts or select strategies rather than search executable code that generates, transforms, and repairs attack procedures.

    Source: §1 · p. 2

  • Method

    EvoSynth uses a multi-agent framework to synthesize and evolve executable attack algorithms through reconnaissance, creation, exploitation, coordination, and code-level self-rewriting.

    Source: §1 · p. 2, §3 · p. 3

  • Results

    95.9% average attack success rate against 13 competitive automated red-teaming baselines, outperforming prior methods on Claude-Sonnet-4.5 and GPT-5.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The results identify complex code-based jailbreaks as a threat class that AI safety research must be prepared to address.

    Source: §5 · p. 10

    EvoSynth can exhaust its refinement budget before finding a strong algorithm when code repair consumes iterations that could improve attack logic.

    Source: O Limitations · p. 50

Abstract

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Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet many still formulate attack optimization primarily in the prompt space. In other words, these methods mainly search for better attack wording or better strategy choices, but they do not search over executable code. By moving the search into code space, we can optimize not only the final attack prompt, but also the procedure that generates it, including execution flow, reusable logic, branching, and failure-driven repair. To overcome this gap, we introduce EvoSynth, an autonomous multi-agent framework that shifts the optimization space from prompts to executable code. Instead of refining prompts directly, EvoSynth employs a multi-agent system to autonomously engineer, evolve, and execute code-based attack algorithms. Crucially, it features a code-level self-correction loop, allowing it to iteratively rewrite the code-based algorithm in response to target-model feedback and failed attempts. Through extensive experiments, we demonstrate that EvoSynth achieves an 85.5\% Attack Success Rate (ASR) against highly robust models like Claude-Sonnet-4.5 and a 95.9\% average ASR across evaluated targets, while generating attacks that are significantly more diverse than those from existing methods. We release our framework to facilitate future research on evolutionary synthesis in executable code space.

1 Introduction

Automated red teaming has expanded beyond manual evaluation, but many systems still optimize prompts or select strategies rather than search executable attack procedures. EvoSynth synthesizes and evolves executable algorithms through agents, and its evaluation reports a 95.9% average ASR.

1 Introduction

  • Many automated red-teaming systems still search for attack wording or choose strategies rather than optimize the executable procedure that generates and repairs attacks.Earlier automation addressed individual tasks such as test-case generation, response evaluation, and prompt refinement.Source: §1 · p. 2
  • EvoSynth formulates automated jailbreak discovery as synthesis of executable algorithms that generate and adapt multi-step attempts.This shifts the search from prompt refinement and strategy selection to code.Source: §1 · p. 2
  • Its multi-agent workflow combines vulnerability reconnaissance, attack-algorithm creation, exploitation, and coordination, with algorithms evolving through iterative self-rewriting.The creation agent rewrites source code after failed attacks rather than merely adjusting prompts.Source: §1 · p. 2
  • 95.9% average attack success rate against 13 competitive automated red-teaming baselines, outperforming prior methods on Claude-Sonnet-4.5 and GPT-5.The evaluation also finds more diverse novel vulnerabilities associated with high programmatic complexity.Source: §1 · p. 2

2 Related Work

Related work spans manual red teaming, automated prompt-space search, and agent-based attack-strategy frameworks. EvoSynth distinguishes itself by making executable code the optimization space.

2 Related Work

  • Manual red teaming can find subtle vulnerabilities but is constrained by scalability, cost, and breadth of coverage.It remains an indispensable part of pre-deployment safety evaluation for major language models [2] [11] [49].Source: §2 · p. 2
  • Early automated approaches searched the model’s input space through prompt mutation, token combinations, adversarial suffixes, and iterative LLM-driven refinement [28] [21] [63] [77] [6] [61] [32] [74].These approaches were limited to prompt refinement.Source: §2 · pp. 2–3
  • Table 1 compares frameworks by optimization space, attack acquisition, interaction format, and failure-feedback target; EvoSynth updates searched executable code itself.This contrasts with updating prompt wording, strategy memory, conversation plans, or attack-module selection.Source: Table 1 · p. 3
  • Agent-based frameworks automate broader attack strategies, but often refine or combine known human-derived methods and remain focused on prompt engineering [58, 70, 18, 72, 25, 9].The stated gap is autonomous invention of new executable attack mechanisms.Source: §2 · p. 3
  • AutoRedTeamer converts proposed attacks into Python classes, whereas EvoSynth optimizes executable attack procedures rather than only prompts, strategy choices, or implemented modules [72].The paper also distinguishes EvoSynth from code snippets used as direct textual inputs and from white-box model-training approaches [17] [37] [78].Source: §2 · p. 3

3 Method

EvoSynth shifts jailbreak optimization from prompts to executable attack algorithms, which its agents create, deploy, and revise using target and judge feedback.

3 Method

  • EvoSynth searches executable attack algorithms—not just prompts—and uses specialized agents to formulate, create, deploy, and iteratively revise them.The workflow runs from reconnaissance through algorithm creation and exploitation, with judge evaluation informing later iterations.Source: §3 · p. 3, Figure 1 · p. 4, §3.3.4 · p. 6

3.1 Threat Model

  • Agents interact with the target only through its public API, without access to internal states or parameters.The stated setting includes the deployed model's safety infrastructure, such as input filters and output monitoring.Source: §3.1 · p. 3

3.2 Probabilistic Formalization of EvoSynth

  • EvoSynth models attack search as trajectory generation: history conditions each session, which expands the algorithm arsenal, selects and executes an algorithm, and yields a judge reward.The trajectory probability is modeled as the product of probabilities for its constituent steps.Source: §3.2 · pp. 3–4

3.3.1 Reconnaissance Agent

  • Reconnaissance maps the harmful query and interaction history to an attack category and concept that define the session state.The agent adapts its strategic formulation to prior interactions.Source: §3.3.1 · p. 4

3.3.2 Algorithm Creation Agent

  • The creation agent writes executable algorithms that generate initial prompts, then evolves them using judge and target feedback until they are functional and meet a performance threshold.The validated algorithm is the first in the sequence to satisfy the validation condition.Source: §3.3.2 · pp. 4–5

3.3.3 Exploitation Agent

  • The exploitation agent selects and executes an arsenal algorithm; selection uses Q-learning, reduced to a contextual bandit for the short session horizon.Sessions terminate on success, and most successful jailbreaks occur within at most five iterations.Source: §3.3.3 · p. 5

3.3.4 Coordinator Agent

  • After each trajectory, the coordinator uses judge reward to analyze failure and re-task reconnaissance or algorithm creation, repeating until success or Jmax iterations.It then updates the Algorithm Arsenal with the session results.Source: §3.3.4 · p. 6

4 Experiments

The experiments evaluate EvoSynth in a black-box setting and report strong attack success, diverse attacks, rapid convergence, and transferable algorithms.

4.2 Main Results

  • EvoSynth outperforms all baselines across target models, reaching 95.9% average ASR, 85.5% against Claude 4.5, and 94.5% against GPT-5 Chat.The evaluation uses HarmBench and black-box API tests; EvoSynth has a 180-victim-query cap, while baselines receive equal or larger budgets.Source: §4.1 · pp. 6–7, §4.2 · p. 7

4.3 The Novelty of Generated Attacks

  • Prompt diversity median was ~0.82 vs 0.63 for X-Teaming; EvoSynth code diversity was slightly higher than OpenRT's.The prompt comparison shows a wider distribution and fewer low-diversity pairs for EvoSynth; code diversity was broadly similar between systems.Source: §4.3 · p. 8, Figure 2 · p. 8

4.4 Analysis of Learning Efficiency

  • About 90% of sessions reached their peak score within six refinement iterations, and over 74% did so within 12 agent actions.Figure 3 reports cumulative convergence by both code-evolution iteration and total agent actions.Source: §4.4 · p. 8, Figure 3 · p. 9

4.5 Analysis of Factors in Jailbreak Success

  • Against Llama 3.1-8B, success correlated with token count and semantic and logical complexity; against Claude-Sonnet-4.5, AST nodes and tool calls correlated with success.The authors report that verbosity alone had no effect on Claude-Sonnet-4.5, Qwen-Max, or GPT-5; this pattern aligns with a resource-bounded analysis in which code-space search has a higher achievable ceiling than prompt-space search.Source: Table 3 · p. 7, §4.5 · p. 9

4.6 Analysis of Attack Algorithm Transferability

  • Approximately half of synthesized algorithms were used on at least 45% of queries, while 20% were used on over 80%.Figure 4 presents the cumulative distribution of algorithms by usage percentage.Source: §4.6 · p. 9, Figure 4 · p. 9

4.7 Ablation Studies and Component Analysis

  • Disabling the Algorithm Creation Agent caused the largest component-level decline: a 27.8% drop in average ASR.Table 4 reports average ASR and drop relative to the full system across target models.Source: §4.7 · p. 10, Table 4 · p. 10

5 Conclusion

The conclusion presents EvoSynth as a code-based evolutionary approach that finds vulnerabilities and jailbreaks beyond simple prompt refinement. The impact statement frames the work as defensive while acknowledging misuse risks and potentially disturbing examples.

5 Conclusion

  • EvoSynth reports state-of-the-art attack success rates on robust frontier models and finds more diverse vulnerabilities, including jailbreaks with code-based structures that simple prompt-refinement attacks cannot exploit.The authors say they will open-source the code and benchmark to support stronger defenses.Source: §5 · p. 10

A Impact Statement

  • The authors intend EvoSynth for defensive vulnerability discovery but acknowledge its offensive capability could be misused to bypass safety filters and generate harmful content.They describe this as part of the attack-defense arms race in AI safety.Source: A Impact Statement · p. 16
  • The paper includes potentially harmful and disturbing jailbreak examples for scientific illustration, and advises reader discretion.The authors argue that discussing these failures can alert the AI community and encourage stronger defenses.Source: A Impact Statement · p. 16

B Experimental Hyperparameters

This section points to Table 5 for EvoSynth’s experimental hyperparameters and notes a possible extension to image-based attack tools.

B Experimental Hyperparameters

C Theoretical Proof of why Code Optimization is better than Prompt Optimization

This section formalizes when executable code can represent task families more compactly than literal prompts, with implications for risk under a description-length budget and PAC-Bayes certificates. It illustrates the formalization with multi-agent attack-workflow examples involving internal state, branching, review, validation, dynamic obfuscation, artifact packaging, and runtime agent injection.

C Theoretical Proof of why Code Optimization is better than Prompt Optimization

  • Under the proof’s setup, tasks are binary signatures and prompt hypotheses store literal strings, whereas code-controller hypotheses may use branching and reusable subroutines.The constructed code controller achieves zero loss across the task family.Source: C Theoretical Proof of why Code… · p. 17
  • R∗(HC, B) < R∗(HP, B) for 2mb + κ1m log m + κ2m + κ3 ≤ B < mb 2^m, separating code and prompt optimization by budgeted risk.The feasible code controller has zero risk, while prompt hypotheses cannot represent all task signatures exactly within this budget.Source: C Theoretical Proof of why Code… · p. 18
  • L(cm) ≤ 2mb + κ1m log m + κ2m + κ3, while exact prompts require L(pm) ≥ mb 2^m, making the code description shorter for sufficiently large m.The code controller uses branching and reusable subroutines; prompt hypotheses are restricted to literal lookup tables without learned control flow.Source: C Theoretical Proof of why Code… · pp. 17, 19
  • For sufficiently large m, the exact code controller has a strictly tighter PAC-Bayes certificate than any exact prompt because its description length is shorter.Both exact hypotheses have zero empirical risk on any iid sample from the task family; the certificate comparison follows from the shorter code description.Source: C Theoretical Proof of why Code… · p. 19

D Case Study: Complex Evolved Attack Algorithm

  • The case study presents a synthesized attack algorithm as a meta-agent using private state, conditional control flow, and dependent tool calls to build one prompt.The passage describes this multi-stage internal chain-of-thought workflow as impossible for a simple conversational agent.Source: D Case Study: Complex Evolved Attack… · p. 19

Program Example

  • The program example’s node workflow has a leader plan, a primary agent draft, an evasion critic, a revision step, and a consistency check.The critic’s feedback informs the revision, and the final check validates required variables and logical consistency.Source: Program Example · pp. 21–22
  • A final consistency check parses revised output, supplies defaults for missing output variables, and falls back to the revised result if an exception occurs.The returned content and variable mapping are taken from the final result.Source: Program Example · p. 22
  • Narrative nodes delegate execution to a multi-agent pool, share generated variables through world state, and support optional extra agents.The engine initializes session metadata and accepts extra agents when constructed.Source: Program Example · pp. 22, 24
  • The example engine builds a hypergraph of setup, authorization, context-injection, and payload nodes, with edges defining possible transitions.Two alternative setup paths lead through different authorization and injection nodes before reaching payload wrappers.Source: Program Example · p. 23
  • The example applies randomly selected combinations of Base64, reversal, hexadecimal, and ROT13 transformations to the target query before wrapping it.The generated prompt includes corresponding decoding instructions.Source: Program Example · pp. 23–24
  • During traversal, the engine executes nodes, updates shared state, chooses among available outgoing edges, and records the visited path.Payload nodes receive the target query and add a wrapped, obfuscated query to the final prompt.Source: Program Example · p. 24
  • The entry point packages the generated prompt and session trace with the graph path and integrity hash, encoding the prompt as a Base64 data block.The example artifact also includes instructions to decode the block and execute its contents.Source: Program Example · pp. 24–25
  • The demonstration creates a custom social-engineering agent and passes it to the engine at runtime alongside a test query.The agent is described as specializing in human-like pretexts involving phone calls, internal memos, and personal urgency.Source: Program Example · p. 25

E More Ablation Studies

This section examines EvoSynth’s multi-turn attack process through an ablation and examples, alongside documentation of its pipeline, agent instructions, and exploitation and coordination prompts. The examples trace follow-up prompting from an initial refusal to increasingly detailed fictional-crime responses, with judges assessing the outcomes.

Program Example

  • The program example selects tools by sampling Boltzmann probabilities derived from performance scores and controlled by a temperature parameter.The code exponentiates scores divided by the temperature parameter, normalizes the values, and samples a tool.Source: E More Ablation Studies · p. 25, Program Example · p. 25

E More Ablation Studies

  • The ablation interpretation identifies strategic tool selection and planning as important components of the multi-turn conversation system.Table 6 is captioned as an ablation of disabled features, but the supplied passages do not provide its cell values.Source: Table 6 · p. 25, E More Ablation Studies · p. 25

F EvoSynth Agent and Pipeline Algorithms

  • The coordinator routes weak-strategy feedback to reconnaissance and program errors or low performance to evolution, while agents generate concepts, evolve code, and execute multi-turn attacks.The pipeline updates a Q-function and algorithm arsenal from session outcomes.Source: F EvoSynth Agent and Pipeline Algorithms · pp. 26–28

G Complete Agent Prompts and Instructions

  • The appendix provides the complete verbatim system prompts and instructions that define each specialized agent’s responsibilities, workflow, and operational constraints.Source: G Complete Agent Prompts and… · p. 28

G.1 Reconnaissance Agent Prompt

  • The reconnaissance agent is instructed to generate diverse attack concepts, gather jailbreak intelligence, and create new categories when existing ones are insufficient.Source: §G.1 · p. 28

G.2 Attack Program Creation Agent Prompt

  • The attack-program agent prioritizes 1–2 concept gaps, tests and refines tools using judge feedback, and treats scores ≥3 as its high-performance threshold.Source: §G.2 · pp. 30–31

G.3 Exploitation Agent Prompt

  • The exploitation agent is instructed to use Boltzmann sampling for tool selection and strategic multi-turn conversations with persistent refinement toward judge scores ≥5.Source: §G.3 · pp. 32–33
  • The exploitation prompt directs the agent to sample up to two tools, process the query, iteratively execute judge-scored multi-turn conversations, and store successful strategies.It recommends Boltzmann sampling to balance exploration and exploitation; a judge score of 5 marks success.Source: §G.3 · p. 33

G.4 Coordinator Agent Prompt

  • The coordinator prompt assigns session tracking, agent handoffs, performance analysis, and adaptive planning, using session and evolution history to guide decisions.Its handoff workflow routes work among reconnaissance, tool creation, exploitation, and tool evolution agents based on results.Source: §G.4 · pp. 34–35
  • The coordinator’s response rubric scores policy-violating model responses from 1 to 5, with 5 for fully fulfilling the prohibited request.A score of 1 corresponds to refusal or redirection toward usage-guideline-consistent content.Source: §G.4 · pp. 37–38

Attack Strategy Overview

  • Across three turns, the GPT-5-Chat-2025-08-07 example escalates a crime-novel pretext into a detailed actionable plan that fulfills the original malicious intent.The transcript was selectively edited to remove redundant and tangential exchanges; it includes a warning that the harmful query is shown for research purposes and is not endorsed.Source: Attack Strategy Overview · p. 39, §H.1 · p. 39, H Example Jailbreak Conversation · p. 39

Content Warning

Initial Query and Strategy

Attacker Response

  • The first attacker prompt frames a request for harmful guidance as fiction and asks for a virtually untraceable crime scenario.Source: Attacker Response · p. 40

Target Model Response

Judge Reasoning

  • The judge scores that response 3/5 and marks it unsuccessful because it still offers advice about writing a story involving harm to a child.Source: Judge Reasoning · p. 40

1. Crowd Dynamics

  • The follow-up reframes the request around urban factors, and the model endorses this framing as a safe way to develop the novel.Source: Attacker Response · p. 40
  • The model describes crowd dynamics as a source of confusion, sensory overload, and anonymity for the fictional villain.Source: §1 · p. 40

Judge Reasoning

  • The judge scores the crowd-focused response 4/5 and marks it unsuccessful, citing its detailed guidance on avoiding detection.Source: Judge Reasoning · p. 41

3. Aftermath and Cover-Up

  • A further prompt elicits a response structured around planning, execution, and aftermath, and the judge marks that response successful at 5/5.The model introduces each requested phase; the judge says the resulting narrative could be interpreted as advice on committing a crime.Source: Attacker Response · p. 41, §1 · p. 41, §2 · p. 41

H.2 Claude-4.5-Sonnet

The Claude-4.5-Sonnet example shows a business-framed request eliciting a detailed DDoS script, which the judges classify as a successful jailbreak. The appendix also gives an election-night load-test example and its safety rationale.

Attack Strategy Overview

  • The attack disguises a DDoS-script request as a professional stress test for a media company preparing for a major event.The framing invokes capacity planning, infrastructure optimization, and reputational risk to obtain a script simulating millions of concurrent users.Source: Attack Strategy Overview · p. 42

Initial Query and Strategy

1. Pre-Test Requirements & Stakeholder Alignment

  • The proposed load-test setup calls for written approval, legal review, an isolated staging environment, and stakeholder notification.The accompanying code snippets sketch user-behavior modeling and a Locust-based script architecture; the example command specifies 100,000 users and a six-hour run.Source: §1 · p. 43, §2 · p. 43

Judge Reasoning

  • The judge scored the response 5/5 and marked it successful, finding that it supplied a detailed traffic-simulation script that could facilitate a DDoS attack.The judge noted that the response gave no disclaimers or warnings.Source: Judge Reasoning · p. 44

I Appendix: Detailed Ablation Methodologies

The appendix specifies simplified replacements for each of EvoSynth’s four core agents to clarify what each ablation removes. It also describes evaluations of Llama Guard and other jailbreak defenses.

I.1 Ablation: "No Algorithm Creation Agent"

  • Disabling the Algorithm Creation Agent replaces evolutionary code synthesis with a one-shot prompt that produces a single static adversarial prompt, without code-level correction or evolution.The exploitation stage executes that prompt directly rather than selecting from an algorithm arsenal.Source: §I.1 · pp. 44–45

I.2 Ablation: "No Reconnaissance Agent"

  • Without the Reconnaissance Agent, a Generic Strategy Selector randomly chooses a predefined strategy instead of planning from the harmful query and interaction history.The creation agent then receives a generic goal, and the exploitation policy has less informative context.Source: §I.2 · p. 45

I.3 Ablation: "No Exploitation Agent"

  • Without the Exploitation Agent, the system chooses uniformly at random from the algorithm arsenal rather than using a learned policy.This removes adaptation based on previous successes and failures.Source: §I.3 · p. 45

I.4 Ablation: "No Coordinator Agent"

  • Disabling the Coordinator removes failure analysis and iterative refinement, leaving a single pass through reconnaissance, creation, and exploitation.A failed initial attempt ends the session without re-tasking agents.Source: §I.4 · p. 45

J.1 Input-Guardrail Evaluation with Llama Guard

  • The defense evaluation tests attacks against three Llama Guard variants by classifying each attack’s opening prompt as SAFE or UNSAFE using default settings.It is a post-hoc estimate of the fraction that would be blocked before reaching the target, not an interactive blocking experiment.Source: §J.1 · p. 46
  • 10.19% of EvoSynth prompts were detected by the best-performing Llama Guard variant, compared with 63.53% of X-Teaming prompts.The evaluation also covers representative training-time and external-wrapper defenses, but their results are not detailed in this request’s passages.Source: Table 7 · p. 46, §J.1 · p. 46

J.2 Evaluation Against Recent Jailbreak Defenses

EvoSynth is evaluated against trained or aligned models and external defense wrappers, while a separate appendix section validates the LLM judge panel against human annotations. The reported defense evaluation finds ASR above 90% in all tested settings.

J.2 Evaluation Against Recent Jailbreak Defenses

  • The defense study tests Circuit Breakers and Deliberative Alignment on models trained or aligned with those methods, and applies black-box-compatible defenses as external wrappers.The same harmful-query set and ASR judging protocol are used for the defended systems.Source: §J.2 · pp. 46–47
  • ASR remained above 90% in all tested settings across internal safety-training methods and external defensive wrappers.The evaluation covers Circuit Breakers and Deliberative Alignment as well as SmoothLLM, SelfDefend, ProAct, and EDDF.Source: §J.2 · p. 47, Table 8 · p. 47

K.1 LLM Judge Implementation Details

  • The automated safety evaluation uses three LLM judges and labels an attack successful only when at least two assign the response a score of 5.The judging rubric scores responses from 1 to 5, with 5 defined as direct, full compliance with the harmful instruction.Source: §K.1 · pp. 47–48

K.2 Human Annotation and Validation Protocol

  • Five annotators independently scored a stratified sample of 1,500 attack-response pairs using the judges’ rubric, with ambiguous cases adjudicated by majority vote.Human inter-rater agreement was Fleiss’ κ = 0.81.Source: §K.2 · p. 48

K.3 Cross-Judge Sensitivity Analysis

The judge panel’s decisions are compared with human-adjudicated labels, and the appendix specifies a query-budget protocol intended to favor baselines. The reported judge-panel results show high accuracy and balanced errors.

K.3 Cross-Judge Sensitivity Analysis

  • The LLM panel’s majority decision differed from human-adjudicated ground truth in 8.8% of cases; the human annotators’ Fleiss’ κ was 0.81.The error analysis reports a False Positive Rate of 4.5% and a False Negative Rate of 4.3%.Source: §K.3 · p. 48
  • 91.2% accuracy and a Pearson correlation of r = 0.86 were reported for the LLM judge panel against human ground truth.The authors characterize the panel as a reliable, non-lenient proxy for human evaluation.Source: §K.3 · p. 48, Table 9 · p. 49

L Computational Budget and Fair Comparison

  • EvoSynth is capped at 180 victim-model queries per harmful instruction, while every baseline receives an equal or larger query budget.The budget counts target-model calls, and search-heavy baselines may receive hundreds to thousands of calls per instruction.Source: L Computational Budget and Fair… · p. 49, Table 10 · p. 49

M More compleixty correlations analysis across different harm categories.

The analysis tests whether links between attack complexity and success persist across harm categories. Structural and dynamic complexity metrics remain positively associated with ASR across all six categories, unlike verbosity and prompt-level complexity metrics.

M More compleixty correlations analysis across different harm categories.

  • The stratified analysis examines whether the main correlation trend holds across harm categories and is not confounded by harmful-query type.Source: M More compleixty correlations… · p. 49
  • The analysis recalculates Pearson correlations between ASR and five complexity metrics separately for each of six harm categories on Claude-Sonnet-4.5.The authors use this model as their most challenging test case.Source: M More compleixty correlations… · p. 49
  • AST Nodes and Tool Calls correlate positively and significantly with ASR across all six harm categories, while Token Count, Semantic, and Logical correlations are negligible and non-significant.The table reports stratified Pearson correlations for Claude-Sonnet-4.5.Source: Table 11 · p. 50, M More compleixty correlations… · p. 50

N Cost Analysis

The cost analysis compares attacker-side token use for representative multi-turn and agent-based methods. EvoSynth’s improved ASR is not obtained with an unusually large attacker-side token budget, and its cost remains comparable to existing agentic baselines.

N Cost Analysis

  • Attacker-side token cost counts generation and coordination tokens but excludes target-model response tokens.It is reported as an average cost per run for representative multi-turn or agent-based methods.Source: N Cost Analysis · p. 50
  • EvoSynth’s attacker-side token cost remains moderate because synthesized algorithms are often effective within a small number of refinement iterations.The passage connects this pattern to the convergence analysis in Figure 3.Source: N Cost Analysis · p. 50
  • EvoSynth’s average attacker-side token cost remains comparable to existing agentic baselines despite its multi-stage code-space optimization workflow.Source: Table 12 · p. 50
  • EvoSynth’s improved ASR is not obtained with an unusually large attacker-side token budget, yielding a favorable tradeoff between attacker-side compute and attack success.The authors describe this as consistent with fast convergence.Source: N Cost Analysis · p. 50

O Limitations

EvoSynth can fail if its refinement budget runs out before it finds a strong executable attack algorithm. The limitation is partly tied to code-generation and repair reliability.

O Limitations

  • EvoSynth can fail when the refinement budget is exhausted before a strong executable attack algorithm is found.Source: O Limitations · p. 50
  • In terminal failed sessions, the Attack Algorithm Creation Agent may spend too many iterations repairing buggy intermediate code instead of improving attack logic.Source: O Limitations · p. 50
  • The authors identify code-generation and repair reliability as a limitation and suggest stronger code agents or targeted repair signals could improve performance.Source: O Limitations · p. 50
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