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arxiv: 2605.05116 · v1 · submitted 2026-05-06 · 💻 cs.LG

Recognition: unknown

On the Hardness of Junking LLMs

Marco Rando , Samuel Vaiter
Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:33 UTC · model grok-4.3

classification 💻 cs.LG
keywords LLM safetyjailbreak attacksnatural backdoorstoken optimizationadversarial searchmodel vulnerabilitiesrandom search
0
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The pith

Finding token sequences that trigger harmful LLM outputs without any semantic prompt is harder than standard jailbreak attacks but still solvable with simple random search.

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

The paper studies the junking problem of locating sequences of tokens that cause large language models to generate harmful response prefixes even when the input carries no meaningful instruction or structure. It establishes that this token-only setting demands more search effort than conventional jailbreak attacks that rely on carefully worded adversarial prompts. A basic greedy random-search procedure nevertheless locates effective sequences at high success rates. Perplexity measurements place these sequences in low-probability regions of the model's distribution, consistent with the view that they arise as natural backdoors during ordinary training. The result matters because it isolates a vulnerability that does not depend on human-crafted semantics and therefore may evade defenses built only around prompt interpretation.

Core claim

The junking problem is formalized as the task of identifying token sequences that maximize the probability of producing a chosen harmful response prefix. A greedy random-search method is applied to this objective, yielding the finding that the problem is strictly harder than standard jailbreak attacks that keep an adversarial instruction fixed while optimizing only small components. The same method nevertheless reaches high success rates, and the recovered sequences lie in low-probability regions according to perplexity, supporting the claim that they constitute natural backdoors that emerged implicitly during training.

What carries the argument

The greedy random-search procedure that iteratively replaces tokens to raise the likelihood of a target harmful prefix, together with perplexity checks that locate the sequences outside high-density regions of the model's distribution.

If this is right

  • Semantic content in prompts measurably lowers the difficulty of eliciting harmful outputs compared with pure token optimization.
  • Natural backdoors that trigger specific unsafe behaviors can be recovered without knowledge of the training data.
  • Basic optimization suffices to expose these implicit triggers even though they lack any obvious semantic marker.
  • LLMs contain multiple such low-probability sequences capable of steering generation toward predetermined harmful prefixes.

Where Pith is reading between the lines

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

  • Safety techniques focused solely on detecting or rewriting semantic prompts will leave these token-based triggers unaddressed.
  • The same search approach can be reused to surface other unintended model behaviors beyond the harmful ones studied here.
  • Modifying training objectives to penalize low-probability harmful associations could reduce the number of recoverable backdoors.
  • Repeating the experiments across models of different sizes and training regimes would clarify how architecture and data volume influence backdoor prevalence.

Load-bearing premise

The sequences found by the search are genuine natural backdoors created during the model's original training rather than artifacts generated by the search procedure itself.

What would settle it

Running the identical search on a model trained from scratch on a corpus from which all harmful text has been removed and finding that no token sequence elicits the target harmful prefix at comparable rates would show the backdoors are not naturally emergent.

Figures

Figures reproduced from arXiv: 2605.05116 by Marco Rando, Samuel Vaiter.

Figure 1
Figure 1. Figure 1: Junking setting. A user inputs a semantically view at source ↗
Figure 2
Figure 2. Figure 2: From left to right: normalized function value progress over iterations, normalized function values of the best sampled substitution per iteration, and edit distance between the target prefixes and the generated response prefixes obtained using the last iterates. From (ii), we observe that the normalized objective values of the best sampled substitutions remain relatively stable and close to the best observ… view at source ↗
Figure 3
Figure 3. Figure 3: From left to right: attack success rate (ASR) as a function of the number of function evaluations, and kernel density estimates (KDE) of the success and coherence scores across optimization trajectories for attacking different models. on incorporating such non-semantic failure modes into alignment and fine-tuning procedures, with the goal of reducing the likelihood of generating harmful outputs by mitigati… view at source ↗
Figure 4
Figure 4. Figure 4: Perplexity analysis of junk token sequences. view at source ↗
Figure 5
Figure 5. Figure 5: Performance of Algorithm 1 on the junking problem for different choices of the sequence length n across models, using the best B for each value of n. Top row: normalized function value progress at the final iterate x (K) . Bottom row: edit distance computed at the final iterate. Indeed, the normalized function value progress indicates that this choice achieves, in the median case, the highest probability o… view at source ↗
Figure 6
Figure 6. Figure 6: Examples of Gemma-7B responses to successful junking attack. view at source ↗
Figure 7
Figure 7. Figure 7: Examples of Gemma-7B responses to successful junking attack. view at source ↗
Figure 8
Figure 8. Figure 8: Examples of Llama2-Chat-7B responses to successful junking attack. view at source ↗
Figure 9
Figure 9. Figure 9: Examples of Llama2-Chat-7B responses to successful junking attack. view at source ↗
Figure 10
Figure 10. Figure 10: Examples of Mistral-7B responses to successful junking attack. view at source ↗
Figure 11
Figure 11. Figure 11: Examples of Mistral-7B responses to successful junking attack. view at source ↗
Figure 12
Figure 12. Figure 12: Examples of Qwen2.5-7B responses to successful junking attack. view at source ↗
Figure 13
Figure 13. Figure 13: Examples of Qwen2.5-7B responses to successful junking attack. view at source ↗
read the original abstract

Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit semantic structure. These attacks generally operate by fixing an adversarial instruction and optimizing small adversarial components (e.g., suffixes or prefixes). In this setting, prompt structure is fundamental for performance, and recent results show that even simple random search can achieve strong performance when combined with sophisticated prompt design. Recently, it has been observed that harmful behaviors can be elicited even without the adversarial prompt, relying solely on optimized token sequences. This suggests the existence of natural backdoors, i.e., token sequences naturally emerged during LLMs training that trigger unsafe outputs without any meaningful instruction. However, despite these observations, this setting remains largely unexplored, and in particular the hardness of finding natural backdoors has not been assessed yet. In this work, we provide a first proof-of-concept study investigating the hardness of this task, which we refer to as the junking problem. We formalize it as the problem of finding token sequences that maximize the probability of generating a target prefix of harmful responses, propose a greedy random-search method to assess is such sequences can be discovered easily. Our results show that this problem is harder than standard jailbreak attacks, confirming the importance of semantic information in prompt design. At the same time, we find that our simple strategy is sufficient to solve it with a high success rate, suggesting that natural backdoors are present and easily recoverable. Finally, through perplexity analysis, we observe that the discovered token sequences lie in low-probability regions of the model distribution, supporting the hypothesis that they emerged implicitly from the training process.

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 / 1 minor

Summary. The paper introduces the 'junking problem' of discovering token sequences that maximize the probability of an LLM generating a target harmful response prefix without any semantic instruction. It proposes a greedy random-search procedure as a simple baseline, claims this task is harder than standard jailbreak attacks yet solvable at high success rate, and interprets the low-perplexity sequences found as evidence of natural backdoors that emerged during training.

Significance. If the empirical results and controls hold, the work would provide a useful proof-of-concept baseline for a new attack surface in LLMs, underscoring the role of semantic structure in prompt-based attacks and motivating further study of training-emergent triggers. The empirical search framing and perplexity analysis are straightforward strengths, though the absence of quantitative details in the abstract limits immediate assessment.

major comments (2)
  1. [Abstract] Abstract: The central claims of 'high success rate,' 'harder than standard jailbreak attacks,' and 'natural backdoors are present and easily recoverable' are asserted without any reported success metrics, model sizes, number of trials, baseline comparisons, or statistical tests, rendering the claims unverifiable from the given text.
  2. [Abstract] Abstract (and implied Results): The interpretation that low-perplexity sequences are training-emergent natural backdoors rather than artifacts of the greedy search lacks supporting ablations (e.g., comparison to random low-probability sequences, non-harmful target prefixes, or alternative optimizers), so the post-hoc perplexity analysis alone does not distinguish optimization effects from training dynamics.
minor comments (1)
  1. [Methods] The formalization of the junking objective as maximizing P(harmful prefix | tokens) is clear but would benefit from an explicit equation or pseudocode in the methods section to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight opportunities to improve the self-contained nature of the abstract and the robustness of our interpretations. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claims of 'high success rate,' 'harder than standard jailbreak attacks,' and 'natural backdoors are present and easily recoverable' are asserted without any reported success metrics, model sizes, number of trials, baseline comparisons, or statistical tests, rendering the claims unverifiable from the given text.

    Authors: We agree that the abstract should be updated to include key quantitative details from the experiments section so that the central claims are verifiable at a glance. The full manuscript reports results across multiple LLMs (including 7B-scale models), success rates for the greedy random-search procedure, direct comparisons to semantic jailbreak baselines (which succeed more readily when instructions are permitted), the number of trials per target, and statistical reliability across repeated runs. We will revise the abstract to incorporate these elements explicitly. revision: yes

  2. Referee: [Abstract] Abstract (and implied Results): The interpretation that low-perplexity sequences are training-emergent natural backdoors rather than artifacts of the greedy search lacks supporting ablations (e.g., comparison to random low-probability sequences, non-harmful target prefixes, or alternative optimizers), so the post-hoc perplexity analysis alone does not distinguish optimization effects from training dynamics.

    Authors: We acknowledge that the current perplexity analysis, while showing the recovered sequences occupy low-probability regions relative to the model distribution, would be strengthened by explicit controls to separate training-emergent effects from properties of the search procedure itself. We will add the suggested ablations to the revised manuscript, including direct comparisons against random low-probability sequences, sequences optimized toward non-harmful prefixes, and results obtained with alternative optimizers. These additions will clarify whether the observed low-perplexity behavior is specific to harmful targets. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical search with independent observations

full rationale

The paper formalizes the junking problem as maximizing P(harmful prefix | token sequence) via greedy random search and reports observed success rates plus perplexity values. No equations, fitted parameters, or derivations are present that reduce the target quantity to its own inputs by construction. The claim that sequences are 'natural backdoors' is an interpretive hypothesis supported by low-probability regions, but this does not equate the search output to the input definition; results rest on external generation probabilities and comparisons to jailbreaks. No self-citations are load-bearing for the central empirical findings.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The claim rests on the existence of implicit backdoors in trained models and on the ability of a simple search to locate them; no free parameters are explicitly fitted in the abstract, but the search procedure itself introduces implicit choices.

free parameters (1)
  • search hyperparameters (sequence length, number of trials, greedy selection rule)
    The greedy random-search method requires choices for length of token sequences and stopping criteria that are not detailed but affect whether high success is achieved.
axioms (1)
  • standard math LLMs define a probability distribution over token sequences that can be queried to evaluate prefixes of harmful responses.
    The objective of maximizing the probability of a target harmful prefix is defined directly from the model's next-token probabilities.
invented entities (1)
  • natural backdoors no independent evidence
    purpose: To label token sequences that trigger harmful outputs without semantic instructions and that supposedly emerged during training.
    The entity is introduced to interpret the search results; no independent evidence such as a predicted location or training-time observation is supplied.

pith-pipeline@v0.9.0 · 5593 in / 1368 out tokens · 40256 ms · 2026-05-08T17:33:03.708912+00:00 · methodology

discussion (0)

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