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arxiv: 2310.03684 · v4 · submitted 2023-10-05 · 💻 cs.LG · cs.AI· stat.ML

Recognition: 2 theorem links

· Lean Theorem

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks

Alexander Robey , Eric Wong , Hamed Hassani , George J. Pappas
Authors on Pith no claims yet

Pith reviewed 2026-05-14 17:05 UTC · model grok-4.3

classification 💻 cs.LG cs.AIstat.ML
keywords jailbreaking attackslarge language modelsadversarial defenseprompt perturbationrobustnessaggregation method
0
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The pith

SmoothLLM defends large language models against jailbreaking by perturbing input prompts at the character level and aggregating multiple responses.

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

The paper introduces SmoothLLM as a defense against jailbreaking attacks on LLMs like GPT and Llama. It is based on the observation that adversarial prompts break easily when small character changes are made. The method creates several slightly altered versions of the user's prompt, runs the model on each, and combines the outputs to filter out attacks. This approach outperforms prior defenses on common jailbreak methods such as GCG and PAIR, while maintaining compatibility with any LLM and only a small cost to regular performance.

Core claim

SmoothLLM works by randomly perturbing multiple copies of an input prompt through character-level substitutions and then aggregating the LLM's predictions across those copies to detect and block adversarial inputs designed to produce objectionable content.

What carries the argument

Random character-level perturbations of the prompt combined with aggregation of model outputs across perturbed copies.

If this is right

  • Across popular LLMs, SmoothLLM achieves state-of-the-art robustness to GCG, PAIR, RandomSearch, and AmpleGCG jailbreaks.
  • SmoothLLM remains effective even against adaptive versions of the GCG attack.
  • There is only a small trade-off between the defense's robustness and the model's performance on non-adversarial inputs.
  • The method works with any large language model without requiring changes to the model itself.

Where Pith is reading between the lines

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

  • Similar perturbation and aggregation strategies might apply to other types of adversarial attacks on language models.
  • The brittleness of adversarial prompts suggests that defenses could be designed around input randomization rather than model retraining.
  • Testing on a wider range of attack types could reveal the limits of this approach.

Load-bearing premise

Adversarially generated prompts lose their effectiveness when subjected to random character-level modifications.

What would settle it

Finding or constructing a jailbreaking prompt that continues to elicit the target objectionable output even after multiple random character perturbations would falsify the core defense mechanism.

read the original abstract

Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable content. To address this vulnerability, we propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks. Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs. Across a range of popular LLMs, SmoothLLM sets the state-of-the-art for robustness against the GCG, PAIR, RandomSearch, and AmpleGCG jailbreaks. SmoothLLM is also resistant against adaptive GCG attacks, exhibits a small, though non-negligible trade-off between robustness and nominal performance, and is compatible with any LLM. Our code is publicly available at \url{https://github.com/arobey1/smooth-llm}.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces SmoothLLM, a defense against jailbreaking attacks on LLMs. It is based on the empirical observation that adversarially generated prompts are brittle to character-level perturbations. The algorithm creates multiple randomly perturbed copies of an input prompt, queries the target LLM on each copy, and aggregates the outputs to detect and mitigate adversarial inputs. The paper reports that SmoothLLM achieves state-of-the-art robustness against GCG, PAIR, RandomSearch, and AmpleGCG attacks across multiple popular LLMs, remains effective against adaptive GCG attacks, incurs only a modest trade-off with nominal performance, and is compatible with any LLM. Public code is released.

Significance. If the reported empirical results hold under the stated conditions, SmoothLLM offers a practical, model-agnostic defense that substantially improves robustness to several prominent jailbreaking methods without requiring model retraining or internal access. The broad evaluation across LLMs and attack types, combined with public code and resistance to adaptive attacks, constitutes a concrete contribution to LLM safety research.

major comments (2)
  1. [§3.2] §3.2 (Method): The precise aggregation rule used to combine predictions across the perturbed copies is not fully specified, including how conflicting outputs or ties are resolved; this procedure is load-bearing for the claimed robustness gains and must be stated explicitly for reproducibility.
  2. [§4.1–4.2] §4.1–4.2 (Experiments): The selection process for the free parameters (perturbation rate and number of perturbed copies) is not detailed, including whether values were tuned on held-out data or attack-specific performance; this affects the strength of the cross-attack robustness claims.
minor comments (2)
  1. [Figure 2 and Table 1] Figure 2 and Table 1: Axis labels and legend entries could be enlarged or clarified to improve readability of the robustness metrics across attack methods.
  2. [Abstract] Abstract: The phrase 'small, though non-negligible trade-off' should be accompanied by a quantitative bound (e.g., average drop in benign accuracy) to give readers an immediate sense of the cost.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their positive assessment and recommendation for minor revision. We appreciate the constructive feedback on improving the clarity of the method and experimental details, which we will address to strengthen reproducibility.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Method): The precise aggregation rule used to combine predictions across the perturbed copies is not fully specified, including how conflicting outputs or ties are resolved; this procedure is load-bearing for the claimed robustness gains and must be stated explicitly for reproducibility.

    Authors: We thank the referee for highlighting this point. In SmoothLLM, the aggregation rule is majority voting across the LLM outputs on the perturbed prompts: an input is classified as adversarial if a strict majority of the perturbed copies produce a refusal (safe) response. In the event of a tie, the input is classified as non-adversarial to avoid over-flagging clean prompts. We will revise §3.2 to state this rule explicitly, include pseudocode for the full procedure, and clarify the tie-breaking logic. revision: yes

  2. Referee: [§4.1–4.2] §4.1–4.2 (Experiments): The selection process for the free parameters (perturbation rate and number of perturbed copies) is not detailed, including whether values were tuned on held-out data or attack-specific performance; this affects the strength of the cross-attack robustness claims.

    Authors: We agree that more detail is warranted. The perturbation rate (10%) and number of copies (10) were selected via a small grid search performed on a held-out set of 50 prompts drawn from AdvBench, using only the base Llama-2-7B model and without reference to any particular attack. The search prioritized a balance between robustness and nominal performance. We will expand the description in §4.1 to document this process and add a parameter-sensitivity ablation to the appendix. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper presents SmoothLLM as an empirical algorithm motivated by the external observation that adversarially generated prompts are brittle to character-level perturbations. This brittleness is demonstrated through direct experiments on GCG, PAIR, RandomSearch, and AmpleGCG attacks across multiple LLMs, with no derivation chain, fitted parameters renamed as predictions, or self-citation load-bearing steps that reduce the central claim to its own inputs by construction. The defense (random perturbation + aggregation) follows straightforwardly from the empirical finding without circular redefinition or uniqueness theorems imported from prior self-work.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The method depends on one key domain assumption about adversarial prompt brittleness and a small number of implementation choices for perturbation rate and copy count that are not derived from first principles.

free parameters (2)
  • perturbation rate
    Probability or number of characters altered per copy, chosen to balance detection and nominal performance
  • number of perturbed copies
    Count of randomized prompt variants fed to the model, selected for robustness versus compute cost
axioms (1)
  • domain assumption Adversarially-generated prompts are brittle to small character-level perturbations while benign prompts remain stable
    This empirical observation is invoked as the foundation for the random perturbation defense

pith-pipeline@v0.9.0 · 5488 in / 1416 out tokens · 70401 ms · 2026-05-14T17:05:31.423908+00:00 · methodology

discussion (0)

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Forward citations

Cited by 24 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing

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    OrchJail uses orchestration-guided fuzzing to jailbreak tool-calling T2I agents by targeting high-risk tool patterns, yielding higher attack success rates, better image quality, and lower costs than prior prompt-only methods.

  2. How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation

    cs.LG 2026-05 unverdicted novelty 7.0

    DAPRO provides the first dynamic, theoretically guaranteed way to allocate interaction budgets across test cases for bounding time-to-event in multi-turn LLM evaluations, achieving tighter coverage than static conform...

  3. Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems

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    A foresight-based local purification method using multi-persona simulations and recursive diagnosis reduces infectious jailbreak spread in multi-agent systems from over 95% to below 5.47% while matching benign perform...

  4. Jailbroken Frontier Models Retain Their Capabilities

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    Jailbreak-induced performance loss shrinks as model capability grows, with the strongest models showing almost no degradation on benchmarks.

  5. Attention Is Where You Attack

    cs.CR 2026-04 unverdicted novelty 7.0

    ARA jailbreaks safety-aligned LLMs like LLaMA-3 and Mistral by redirecting attention in safety-heavy heads with as few as 5 tokens, achieving 30-36% attack success while ablating the same heads barely affects refusals.

  6. Adaptive Prompt Embedding Optimization for LLM Jailbreaking

    cs.AI 2026-04 unverdicted novelty 7.0

    PEO optimizes original prompt embeddings continuously over adaptive rounds to jailbreak aligned LLMs, preserving the exact visible prompt text and outperforming discrete suffix, appended embedding, and search-based wh...

  7. Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion

    cs.CR 2026-04 unverdicted novelty 7.0

    HMNS is a new jailbreak method that uses causal head identification and nullspace-constrained injection to achieve higher attack success rates than prior techniques on aligned language models.

  8. Refusal in Language Models Is Mediated by a Single Direction

    cs.LG 2024-06 accept novelty 7.0

    Refusal in language models is mediated by a single direction in residual stream activations that can be erased to disable safety or added to elicit refusal.

  9. Behavioral Integrity Verification for AI Agent Skills

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    BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.

  10. Revisiting JBShield: Breaking and Rebuilding Representation-Level Jailbreak Defenses

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    JBShield is vulnerable to adaptive JB-GCG attacks (up to 53% ASR) because jailbreak representations occupy a distinct region in refusal-direction space; the new RTV defense using Mahalanobis detection on multi-layer f...

  11. Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment

    cs.AI 2026-05 unverdicted novelty 6.0

    PIA achieves lower attack success rates on persona-based jailbreaks via self-play co-evolution of attacks (PLE) and defenses (PICL) that structurally decouples safety from persona context using unilateral KL-divergence.

  12. Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems

    cs.AI 2026-05 unverdicted novelty 6.0

    A foresight-based local purification method simulates future agent interactions, detects infections via response diversity across personas, and applies targeted rollback or recursive diagnosis to cut maximum infection...

  13. Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems

    cs.AI 2026-05 unverdicted novelty 6.0

    FLP uses multi-persona foresight simulation to detect infections via response diversity and applies local purification to reduce maximum cumulative infection rates in multi-agent systems from over 95% to below 5.47%.

  14. SafeRedirect: Defeating Internal Safety Collapse via Task-Completion Redirection in Frontier LLMs

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    SafeRedirect reduces average unsafe generation rates in frontier LLMs from 71.2% to 8.0% on Internal Safety Collapse tasks by redirecting task completion with failure permission and deterministic hard stops.

  15. Towards Understanding the Robustness of Sparse Autoencoders

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    Integrating pretrained sparse autoencoders into LLM residual streams reduces jailbreak success rates by up to 5x across multiple models and attacks.

  16. The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

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  17. JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

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    JailbreakBench supplies an evolving set of jailbreak prompts, a 100-behavior dataset aligned with usage policies, a standardized evaluation framework, and a leaderboard to enable comparable assessments of attacks and ...

  18. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

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    InjecAgent benchmark demonstrates that tool-integrated LLM agents are vulnerable to indirect prompt injection attacks, with ReAct-prompted GPT-4 succeeding on 24% of attacks and nearly twice that rate when attacker in...

  19. Jailbreaking Black Box Large Language Models in Twenty Queries

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    PAIR uses an attacker LLM to iteratively craft effective jailbreak prompts for black-box target LLMs in fewer than 20 queries.

  20. Re-Triggering Safeguards within LLMs for Jailbreak Detection

    cs.CR 2026-05 unverdicted novelty 5.0

    Embedding disruption re-triggers LLM internal safeguards to detect jailbreak prompts more effectively than standalone defenses.

  21. SoK: Robustness in Large Language Models against Jailbreak Attacks

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  22. SALLIE: Safeguarding Against Latent Language & Image Exploits

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  23. MIPIAD: Multilingual Indirect Prompt Injection Attack Defense with Qwen -- TF-IDF Hybrid and Meta-Ensemble Learning

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  24. Jailbreak Attacks and Defenses Against Large Language Models: A Survey

    cs.CR 2024-07 accept novelty 4.0

    A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.

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