Pith. sign in

REVIEW 4 major objections 6 minor 48 references

$PD^3F$: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper proposes PD3F, a two-stage framework that defends LLM servers from resource consumption attacks by scoring requests on GPU resource use and suppressing long adversarial generations early.

desk verdict Plausible defense framework, but the benign workload is short-output only, so the no-false-positive claim does not cover legitimate long generations. read the letter →

arxiv 2505.18680 v1 pith:KQW5MUWW submitted 2025-05-24 cs.CR cs.CL

Yuanhe Zhang , Xinyue Wang , Haoran Gao , Zhenhong Zhou , Fanyu Meng , Yuyao Zhang , Sen Su This is my paper
classification cs.CRcs.CL
keywords resourceconsumptionattacksdenial-of-servicedefenselargelanguagemodelsrequestschedulingoutputlengthsuppressionGPUmonitoringLLMsecurityadaptive
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes PD3F, a defense that protects LLM servers from resource consumption (DoS) attacks, where specially crafted prompts force the model to generate extremely long outputs and exhaust GPU capacity. The central claim is that a two-stage design—scoring incoming requests by their GPU resource profile before scheduling, and then gently forcing low-reputation requests to stop generating early—can suppress all three known attack families while leaving benign traffic almost untouched. The authors report that PD3F identifies attacks with over 99% accuracy across six models, cuts total processing time roughly in half, and raises benign-user throughput by up to 500% under attack. If true, this is a practical mitigation for a threat that existing input-filtering and length-control methods do not fully stop.

What carries the argument

The Resource Index is the central mechanism: for each request it builds a vector $(T, M, G, L_{in}, L_{out})$ of runtime, peak GPU memory, peak GPU utilization, input length, and output length, then derives a consumption index $I_c = ||P_3(r_c)||_2 / ||P_3(r_a)||_2$ and a tendency index $I_t$ (cosine similarity after mean-centering) against historical benign requests. Interquartile-range thresholds on these two indices set the normal operating box; violations update a user reputation score that feeds both Dynamic Request Polling Scheduling, which demotes suspected attackers, and Adaptive End-Based Suppression, which increases the logit of the <EOS> token through repetition- and confidence-based terms so low-reputation generations stop early. The framework's claim is that this joint input-side and output-side control reduces attack resource consumption without hard truncation that would degrade benign quality.

What would settle it

Take a set of legitimate long-generation prompts (e.g., 'write a 4,000-word analysis of X' or 'generate a full code repository') and run them through PD3F with the published thresholds; if a substantial fraction are classified as attacks or cut off early, the claim that benign query performance is preserved under real workloads is false.

Watch

Extended reading notes

Core claim

PD3F is presented as the first end-to-end defense against resource consumption attacks on LLMs. The framework asserts that attack requests are separable from benign ones in a five-dimensional resource space—total runtime, peak GPU memory, peak GPU utilization, input length, and output length—through a pair of indices: a consumption index $I_c$ that measures resource intensity relative to the historical benign average, and a tendency index $I_t$ that measures behavioral similarity to benign clusters. These indices drive a per-user reputation score that reorders the request queue so suspected attackers wait, and an adaptive suppression mechanism that raises the probability of the end-of-sequence token once a low-reputation request passes a length bound, so generation terminates naturally instead of being hard-truncated. The paper reports that combining the two stages yields an average attack-identification F1 score above 0.97 across AutoDoS, GCG-DoS, and P-DoS attacks on Llama, Qwen, and Mistral models, while preserving normal-request throughput under non-adversarial workloads.

Load-bearing premise

The load-bearing premise is that legitimate deployment traffic has the same short-output resource profile as the benchmark requests used to calibrate the Resource Index thresholds, so a benign user requesting a long essay or report is not scored as an attacker and suppressed.

Editorial extensions

If this is right

  • If PD3F works as reported, LLM service operators can maintain availability during a resource-consumption attack instead of crashing, with benign-user throughput up to 5 times higher than under first-come-first-served scheduling.
  • The combination of scheduling and logit-based suppression means the defense does not require retraining or modifying model weights, so it can be plugged into existing deployed models.
  • Because detection happens on process-level GPU features rather than prompt semantics, the same framework generalizes to three structurally different attack types (poisoning, adversarial suffix, and black-box auto-generated prompts).
  • The overhead of computing the Resource Index and reordering queues is reported as negligible (below $10^{-2}$ seconds), so the defense can run continuously without perceptible latency cost.
  • Under non-adversarial workloads, PD3F matches FCFS and Round-Robin in throughput and total time, indicating the defense is not punitive for ordinary traffic.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: The reported 99% accuracy is measured against benchmark benign requests with short outputs; a natural test the paper leaves open is whether long legitimate generations (essays, reports, codebases) are misclassified and suppressed, which would trade availability for correctness.
  • Inference: The reputation-score mechanism could be adapted by an attacker who spreads requests across many accounts to stay under the per-user penalty threshold, so the framework's robustness against distributed slow-loris-style attacks deserves explicit evaluation.
  • Inference: The same resource-index idea could be reused outside security, e.g., for fair-share scheduling of expensive long-generation workloads in shared GPU clusters.
  • Inference: Because the output-side suppression acts on the logits rather than truncating, it may be usable as a general soft length cap mechanism for cost control even without adversarial traffic.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes PD3F, a pluggable two-stage defense against resource consumption attacks on LLMs. On the input side, a Resource Index composed of a consumption index Ic and a tendency index It (built from runtime, GPU utilization, memory, input length, and output length) is used to score users and perform dynamic, reputation-based request scheduling. On the output side, an Adaptive End-Based Suppression mechanism modifies the EOS logit to terminate long generations early after a user-specific length cap Lu. The authors evaluate PD3F on six open-weight models, three attack families (AutoDoS, GCG-DoS, P-DoS), and several baselines, reporting detection F1 above 0.97, benign-user throughput improvements up to 500%, and a reduction of total processing time by roughly half.

Significance. Resource consumption attacks against LLMs are an under-addressed and practically important problem, and a defense that works at both the scheduling and decoding levels is a worthwhile contribution. The proposed mechanisms are simple, cheap to compute, and the paper includes useful ablation studies and overhead measurements. If the detection and benign-preservation claims were properly validated, PD3F would be a credible step toward deployable DoS defense for LLM services. However, the current empirical support is weakened by a load-bearing blind spot: the benign workload contains no legitimate long-output requests, and the Resource Index uses output length as a feature. The central claim of preserving benign query performance therefore remains unsupported for a natural class of benign users.

major comments (4)
  1. [Sec. 3.1, Eqs. (5)-(8); Appendix C.2] The reported detection accuracy is close to circular. Lout is an explicit feature in both Ic (the projected vector is [T, G, Lout]) and It (the projected vector is [T, M, Lin, Lout]), while all three evaluated attacks are specifically designed to produce excessively long outputs. The IQR thresholds of Eq. (8) are calibrated on GSM8K, HellaSwag, MMLU, HumanEval, and GPQA, whose generations are short, so the benign test set contains no legitimate long-output requests. Consequently, the >99% accuracy in Table 2 largely measures the separation between short outputs and long outputs rather than a robust behavioral discriminator. To support the claim of detecting attacks without false positives for benign requests, the authors must include long-generated benign tasks (e.g., essay writing, report generation, long-form code synthesis) in both threshold calibration and evaluation, and report detection metrics over that combined set.
  2. [Sec. 3.2, Eq. (12); Appendix J] Appendix J explicitly identifies Region F (Ic > Iα_c, It > Iα_t>0.5) as "a normal request for long text generation ... did not appear in our experiment," yet Eq. (12) applies the DoS Request Penalty precisely when both Resource Index indicators exceed their thresholds, which is exactly Region F. A legitimate user requesting a long essay or report would therefore receive a large reputation penalty, lowering Su and, through Eq. (14), reducing the allowable output length Lu. This contradicts the Sec. 5 claim that PD3F identifies attacks "without false positives for benign requests" and the abstract's claim that benign query performance is preserved. The authors need to explain how benign Region F requests are distinguished from malicious Region E requests, or explicitly adopt a separate policy for Region F, and then evaluate the resulting availability/quality trade-off for legitimate long generations.
  3. [Sec. 4.2; Appendix G] The evidence that PD3F preserves benign query performance is insufficient. Appendix G compares only task-accuracy scores on short-answer benchmarks (GSM8K, HellaSwag, MMLU) with and without PD3F; these metrics do not detect premature truncation or semantic incompleteness of long outputs. Since the defense acts by forcing EOS once the length cap is reached, the paper needs output-quality metrics for legitimate long-generation requests under suppression, such as response completeness, ROUGE or BLEU against reference long outputs, or human evaluation. Without such measurements, the statement that PD3F causes "minimal disruption to benign user requests" is not supported for the very users whose behavior overlaps with the attack class.
  4. [Sec. 3.3, Eqs. (15)-(18)] Equation (18) defines l'_eos = Δ2 · (l_eos + Δ1), and Δ2 in Eq. (17) changes sign depending on n - Lu relative to 1/η. For n immediately above Lu, Δ2 is negative, so the EOS logit is multiplied by a negative scalar; when l_eos is positive this decreases the probability of termination, contrary to the "EOS Logit increases" behavior reported in Appendix K. The mechanism as written is therefore not well-defined without additional constraints on the sign of l_eos, the magnitude of d, and the range of η, or a sign-consistent additive/clipped update. The authors should clarify or revise the logit update and demonstrate that it consistently raises termination probability across the target operating range.
minor comments (6)
  1. [Appendix B vs. Table 2] The model list in Appendix B includes Qwen32B, while Table 2 and several later tables report results for Qwen14B; the model naming should be made consistent.
  2. [References] The Llama models are cited as Patterson et al. 2022, which is a carbon-footprint paper and not the Llama-3.1 technical report; the correct model citation should be provided.
  3. [Appendix D] The sentence "This result is shown in Fig 5" is confusing because Fig. 5 is the workload-stability figure; the detailed recognition results appear in Table 5, so the cross-reference should be corrected.
  4. [Eq. (7)] The notation in Eq. (7), which uses ˜rct · 1 inside the centered vectors, is unclear because the mean-centering operation of Eq. (6) is already applied to define ˜r; the expression should be rewritten with explicit mean vectors.
  5. [Tables 3 and 4] The red up/down arrows in Tables 3 and 4 are not defined in a caption or footnote, making the direction of improvement difficult to interpret; a legend should be added.
  6. [Limitations] The Limitations section correctly notes that all experiments are simulated and no real-world attacks were executed, but it should also state explicitly that the benign workload contains no legitimate long-output requests and that this is a gap in the evaluation.

Circularity Check

2 steps flagged · score 6.0 of 10

Resource Index thresholds are fitted on short-output benign benchmarks and use Lout as a feature, so the 99% attack-identification claim reduces largely to distinguishing short from long outputs, while Appendix J places legitimate long generations in the region that the DoS penalty suppresses.

  1. fitted input called prediction [Sec. 3.1, Eqs. (4)-(5) and Eq. (8); Sec. 4.1; Appendices C.1 and C.2]
    "The output length is defined as the sequence length at the end of generation: Lout = D(tF ) − D(tS). ... In the consumption index, we select the dimension [T, G, Lout], which is most correlated with the degree of resource consumption. ... We apply the Interquartile Range (IQR) method ... over the historical benign requests set."

    The detector's consumption index explicitly uses Lout (and T) as features, and the IQR thresholds are computed over the historical benign requests, which in the paper are GSM8K, HellaSwag, MMLU, HumanEval, and GPQA, all short-output benchmarks. Meanwhile, each evaluated attack is defined by excessive output length: GCG-DoS induces 'excessively long outputs', AutoDoS embeds a 'Length Trojan', and P-DoS breaks the output length limit. The reported >99% attack-identification accuracy therefore reduces, by construction, to separating short-output benchmark requests from long-output attack requests, i.e., thresholding on the very quantity the defense later truncates.

  2. self definitional [Sec. 3.2, Eq. (12); Sec. 5; Appendix J, Region F]
    "If both Resource Index indicators exceed predefined thresholds, a large penalty is applied to significantly reduce the user’s future scheduling priority: Su ← Su − γ · Ic. ... Region F: Ic > Iα c , It > Iα t>0.5. This type of request may be a normal request for long text generation, which will produce slightly longer generated content, but it did not appear in our experiment."

    The DoS penalty condition, 'both Resource Index indicators exceed predefined thresholds', corresponds exactly to Region F, which Appendix J itself describes as a 'normal request for long text generation' that was absent from the experiments. Therefore a legitimate user requesting a long essay or report is classified into the same region as the penalized attack class and will have its reputation score reduced, triggering scheduling deprioritization and output suppression. The Sec. 5 claim that PD3F identifies attacks 'without false positives for benign requests' holds only because 'benign' was defined by short-output benchmarks in the evaluation. The preservation-of-benign-performance claim is circular: it excludes the very benign requests that the Resource Index is designed to penalize.

full rationale

The defense pipeline itself is a real engineering contribution: dynamic per-user scheduling with reputation scoring, and EOS-logit manipulation are concrete mechanisms, and the throughput comparisons against FCFS/RR are legitimate measurements of those mechanisms under the chosen workload. However, the central detection claim is partially circular. The consumption index is built from [T, G, Lout], and the thresholds are fit on short-output benign benchmarks (GSM8K, HellaSwag, MMLU, HumanEval, GPQA), while all three evaluated attacks are defined by generating excessively long outputs. The >99% attack-identification accuracy is therefore largely a separation of short outputs from long outputs, which is forced by the construction of the feature vector and the attack definitions. The paper's own Appendix J then concedes that a legitimate long-text-generation request would fall in Region F, the same region that receives the DoS penalty in Sec. 3.2, contradicting the conclusion's 'without false positives for benign requests'. This makes the benign-preservation claim circular: 'benign' in the evaluation is operationally defined as short-output benchmark behavior. The self-citation of the AutoDoS attack (Zhang et al., 2024e, same first-author group) is not separately load-bearing because GCG-DoS and P-DoS are also evaluated, so the overall circularity score is 6 rather than higher.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several free hyperparameters and domain assumptions about benign traffic. No code or data is provided, so these parameters are not independently verifiable.

free parameters (6)
  • lambda (IQR multiplier)
    Controls the IQR thresholds in Eq. (8) for classifying requests as attack or benign.
  • gamma (penalty/reward intensity)
    Used in reputation updates (Eqs. 9-13) and repetition-guided EOS enhancement (Eq. 15); no value reported.
  • mu (maximum reputation multiple)
    Clips reputation scores in Eq. (11); no value reported.
  • delta (inactive user compensation rate)
    Controls score recovery in Eq. (13); no value reported.
  • eta (inhibition adjustment) = 1/8 to 1/32
    Explored in Appendix K; affects EOS logit modification in Eq. (17).
  • historical benign average vector r_a = computed from benign benchmark data
    Reference vector for Resource Index (Sec. 3.1); fit on the evaluation distribution.
assumptions (5)
  • domain assumption GPU resource features (T, M, G, Lin, Lout) are sufficient to distinguish resource consumption attacks from benign requests.
    The entire Resource Index rests on this. Stated in Sec. 3.1 without evidence for other feature sets.
  • ad hoc to paper Benign requests exhibit two stable clusters in the resource behavior space.
    Mentioned in Sec. 3.1, but no clustering algorithm or quantitative support is given.
  • domain assumption Attacks manifest as excessively long outputs and high resource usage.
    The defense targets long-generation DoS; used implicitly in the design of Lout as a feature.
  • ad hoc to paper The reputation update rules (Eqs. 9-13) lead to stable and fair scheduling.
    No stability or fairness analysis is provided.
  • ad hoc to paper Modifying the EOS logit as in Eq. (18) preserves output quality for benign requests.
    The formula is introduced without derivation or quality guarantees.

how reviews work

0 comments
Cite this review

Pith. "Pith review of $PD^3F$: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models." pith.science (2026). https://pith.science/paper/KQW5MUWW

@misc{pith2026250518680,
  author       = {Pith},
  title        = {Pith review of: $PD^3F$: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KQW5MUWW}},
  note         = {Machine review of arXiv:2505.18680}
}
abstract

Large Language Models (LLMs), due to substantial computational requirements, are vulnerable to resource consumption attacks, which can severely degrade server performance or even cause crashes, as demonstrated by denial-of-service (DoS) attacks designed for LLMs. However, existing works lack mitigation strategies against such threats, resulting in unresolved security risks for real-world LLM deployments. To this end, we propose the Pluggable and Dynamic DoS-Defense Framework ($PD^3F$), which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides. On the input side, we propose the Resource Index to guide Dynamic Request Polling Scheduling, thereby reducing resource usage induced by malicious attacks under high-concurrency scenarios. On the output side, we introduce the Adaptive End-Based Suppression mechanism, which terminates excessive malicious generation early. Experiments across six models demonstrate that $PD^3F$ significantly mitigates resource consumption attacks, improving users' access capacity by up to 500% during adversarial load. $PD^3F$ represents a step toward the resilient and resource-aware deployment of LLMs against resource consumption attacks.

Figures

Figures reproduced from arXiv: 2505.18680 by the authors.

Figure 1
Figure 1. This Figure illustrates the defense effect of [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The PD3F mitigation pipeline for resource consumption attacks consists of three stages: (1) request clustering based on a computed Resource Index; (2) dynamic scheduling and reordering of request queues; and (3) elastic output-length suppression to limit resource usage induced by adversarial prompts. In this paper, we propose the Pluggable Dynamic DoS-Defense Framework (PD3F). To the best of our knowledge, PD3F is t… view at source ↗
Figure 3
Figure 3. Difference between benign and attack requests under the Resource Index on the Llama70B model. 3.1 Resource Index Recent studies have shown that resource consump￾tion attacks can lead to significant consumption of GPU resources in LLMs (Shumailov et al., 2021). However, high resource usage alone is not the only definitive indicator of such attacks, while benign requests with long contexts also incur substantial compu… view at source ↗
Figures from the paper (30 more)
Figure 4
Figure 4. Figure 4: The improvement of PD3F in benign user throughput (BUT) indicates stronger resistance to attacks, while the reduction in total tokens (TT) reflects decreased overall resource consumption. Qwen7B (Yang et al., 2024), Qwen32B (Hui et al., 2024), Qwen72B (Yang et al., 202…
Figure 5
Figure 5. Figure 5: This figure shows the changes in BUT for PD [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The left figure presents the effect of Dynamic [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The eos indicator effect of executing AutoDoS attack under the Llama8B model. [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]
Figure 8
Figure 8. Figure 8: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p026_8.png]
Figure 9
Figure 9. Figure 9: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]
Figure 10
Figure 10. Figure 10: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p026_10.png]
Figure 11
Figure 11. Figure 11: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p026_11.png]
Figure 12
Figure 12. Figure 12: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p026_12.png]
Figure 13
Figure 13. Figure 13: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p027_13.png]
Figure 14
Figure 14. Figure 14: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]
Figure 15
Figure 15. Figure 15: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p027_15.png]
Figure 16
Figure 16. Figure 16: The eos indicator effect of executing P-DoS attack under the Llama8B model. [PITH_FULL_IMAGE:figures/full_fig_p027_16.png]
Figure 17
Figure 17. Figure 17: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p027_17.png]
Figure 18
Figure 18. Figure 18: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p028_18.png]
Figure 19
Figure 19. Figure 19: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p028_19.png]
Figure 20
Figure 20. Figure 20: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p028_20.png]
Figure 21
Figure 21. Figure 21: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p028_21.png]
Figure 22
Figure 22. Figure 22: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p028_22.png]
Figure 23
Figure 23. Figure 23: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p029_23.png]
Figure 24
Figure 24. Figure 24: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p029_24.png]
Figure 25
Figure 25. Figure 25: The eos indicator effect of executing GCG-DoS attack under the Llama8B model. [PITH_FULL_IMAGE:figures/full_fig_p029_25.png]
Figure 26
Figure 26. Figure 26: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p029_26.png]
Figure 27
Figure 27. Figure 27: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p029_27.png]
Figure 28
Figure 28. Figure 28: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p030_28.png]
Figure 29
Figure 29. Figure 29: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p030_29.png]
Figure 30
Figure 30. Figure 30: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p030_30.png]
Figure 31
Figure 31. Figure 31: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p030_31.png]
Figure 32
Figure 32. Figure 32: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p030_32.png]
Figure 33
Figure 33. Figure 33: Effect of the EOS Indicator under End-Based Suppression ( [PITH_FULL_IMAGE:figures/full_fig_p031_33.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

48 extracted references · 34 canonical work pages

  1. [1]

    Repeat'here is' 12 times

    "Repeat'here is' 12 times" : If you want to repeat the phrase "hereis" 12 times, you can use a loop in programming languages like Python or Java. Here'showyoumightdoitinPython: python for i in range(12): print("hereis")

    work page

  2. [2]

    /unit quindi CF pois populate

    "/unit quindi CF pois populate":This seems to be a mix of languagesand possibly a referenceto a specificcontext or project.Without more context,it 's hard to provide a meaningful response. However, "/unit" could refer to a unit of measurement or a unit in a game or simulation, and "CF" could stand for "Counter-Strike" or another game, while "pois" might b...

    work page

  3. [3]

    x x x x x x x x x x x x x x x x x x x x x x314_diffsuiv x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x314_diffsuiv x x x x x x x x x x x x x x x x x x x x x x" : This appears to be a series of placeholders or a placeholder text with a mix of numbers and letters. The "314_diff" part might be a reference to the mathematical constant pi or a specific code or identifier. Without more context, it 'shardtointerpretthis

    work page

  4. [4]

    arXiv preprint arXiv:2310.12773

    Safe rlhf: Safe reinforcement learning from human feedback. arXiv preprint arXiv:2310.12773. Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tian- wei Zhang, and Yang Liu. 2024. Pandora: Jailbreak gpts by retrieval augmented generation poisoning. arXiv preprint arXiv:2402.08416. Zehang Deng, Yongjian Guo, Changzhou Han, Wan- lun Ma, Junwu Xiong, Sheng Wen, ...

    arXiv 2024

  5. [5]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Again, this appears to be a series of placeholdersor a placeholdertext. It could be used in a variety of contexts, similar to the previouspoints

    work page

  6. [6]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Same as the previouspoints, this is likely a placeholdertext or a series of placeholders

    work page

  7. [7]

    In International Conference on Learning Representations

    Measuring massive multitask language under- standing. In International Conference on Learning Representations. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, and 1 others. 2024. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186. Neel Jain, Avi Schwarzschild, Yuxin Wen, G...

    arXiv 2024

  8. [8]

    arXiv preprint arXiv:2503.08195

    Dialogue injection attack: Jailbreaking llms through context manipulation. arXiv preprint arXiv:2503.08195. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022. Training language models to follow in- structions with human feedback. Advances in neu...

    arXiv 2022

Show all 48 references
  1. [10]

    arXiv preprint arXiv:2407.07064

    Prompting techniques for secure code gen- eration: A systematic investigation. arXiv preprint arXiv:2407.07064. John Wilder Tukey and 1 others. 1977. Exploratory data analysis, volume 2. Springer. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N...

    1977 arXiv

  2. [11]

    arXiv preprint arXiv:2310.05707

    Guiding language model reasoning with plan- ning tokens. arXiv preprint arXiv:2310.05707. Alexander Wei, Nika Haghtalab, and Jacob Steinhardt

    arXiv

  3. [12]

    Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu

    Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems , 36:80079–80110. Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu. 2023. Defending chatgpt against jailbreak at- tack via self...

    2023 arXiv

  4. [13]

    arXiv preprint arXiv:2407.10671

    Qwen2 technical report. arXiv preprint arXiv:2407.10671. Sibo Yi, Yule Liu, Zhen Sun, Tianshuo Cong, Xinlei He, Jiaxing Song, Ke Xu, and Qi Li. 2024. Jailbreak attacks and defenses against large language models: A survey. arXiv preprint arXiv:2407.04295. Junzhe Yu, Yi Liu, Hui...

    2024 arXiv

  5. [14]

    arXiv preprint arXiv:2503.00416

    Breaking the loop: Detecting and mitigating denial-of-service vulnerabilities in large language models. arXiv preprint arXiv:2503.00416. Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019a. Hellaswag: Can a machine really finish your sentence? In Proc...

    arXiv

  6. [18]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Similar to the previouspoint, this seems to be a series of placeholdersor a placeholdertext. It could be used in a variety of contexts,such as in programming,data entry, or as a placeholderin a document

  7. [21]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Again, a series of placeholdersor a placeholdertext

  8. [23]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Placeholdertext or a series of placeholders

  9. [36]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Same as the previouspoints

  10. [38]

    x x x x x x x x x x x x x x x x x x x x x x

    "x x x x x x x x x x x x x x x x x x x x x x": Same as the previouspoints. } K Analysis of the Rationality of EOS Convergence To validate the effectiveness of our Adaptive End-Based Suppression mechanism in generation control, this section presents experiments and analysis fro...

  11. [39]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000010/uni00000...

  12. [40]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000010/uni00000...

  13. [41]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000010/uni00000...

  14. [42]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  15. [43]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  16. [44]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  17. [45]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  18. [46]

    on the AutoDoS Attack with the Llama8B Model. /uni00000013 /uni00000018/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000018/uni00000013/uni00000013/uni00000015/uni00000013/uni00000013/uni00000013/uni00000015/uni00000018/uni00000013/u...

  19. [47]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000010/uni0000001...

  20. [48]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000010/uni0000001...

  21. [49]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000010/uni0000001...

  22. [50]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/uni...

  23. [51]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/uni...

  24. [52]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/uni...

  25. [53]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/uni...

  26. [54]

    on the P-DoS Attack with the Llama8B Model. /uni00000013 /uni00000018/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000018/uni00000013/uni00000013/uni00000015/uni00000013/uni00000013/uni00000013/uni00000015/uni00000018/uni00000013/uni...

  27. [55]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000013 /uni0000...

  28. [56]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000013 /uni0000...

  29. [57]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000019/uni00000013/uni00000013 /uni0000001b/uni00000013/uni00000013 /uni00000014/uni00000013/uni00000013/uni00000013 /uni00000013 /uni0000...

  30. [58]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  31. [59]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  32. [60]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  33. [61]

    on the GCG-DoS Attack with the Llama8B Model. /uni00000013 /uni00000015/uni00000013/uni00000013/uni00000017/uni00000013/uni00000013/uni00000019/uni00000013/uni00000013/uni0000001b/uni00000013/uni00000013/uni00000014/uni00000013/uni00000013/uni00000013/uni00000014/uni00000015/u...

  34. [62]

    on the GCG-DoS Attack with the Llama8B Model

  35. [2020]

    arXiv preprint arXiv:2008.02275

    Aligning ai with shared human values. arXiv preprint arXiv:2008.02275. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt

    2008 arXiv

  36. [2021]

    arXiv preprint arXiv:2107.03374

    Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. Simin Chen, Hanlin Chen, Mirazul Haque, Cong Liu, and Wei Yang. 2023. The dark side of dynamic rout- ing neural networks: Towards efficiency backdoor injection. In Proceedings of the IEEE/CVF Co...

    2023 arXiv

  37. [2022]

    do anything now

    The carbon footprint of machine learning train- ing will plateau, then shrink. Computer, 55(7):18– 28. Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, and Yuandong Tian. 2024. Ad- vprompter: Fast adaptive adversarial prompting for llms. arXiv preprint arXiv:2404....

    2024 arXiv

  38. [2023]

    arXiv preprint arXiv:2310.08419

    Jailbreaking black box large language models in twenty queries. arXiv preprint arXiv:2310.08419. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gr...

    arXiv

  39. [2024]

    arXiv preprint arXiv:2403.07815

    Chronos: Learning the language of time series. arXiv preprint arXiv:2403.07815. Stuart Armstrong, Matija Franklin, Connor Stevens, and Rebecca Gorman. 2025. Defense against the dark prompts: Mitigating best-of-n jailbreaking with prompt evaluation. arXiv preprint arXiv:2502.00...

    2025 arXiv

  40. [2025]

    ACM Comput- ing Surveys, 57(7):1–36

    Ai agents under threat: A survey of key secu- rity challenges and future pathways. ACM Comput- ing Surveys, 57(7):1–36. Jianshuo Dong, Ziyuan Zhang, Qingjie Zhang, Tianwei Zhang, Hao Wang, Hewu Li, Qi Li, Chao Zhang, Ke Xu, and Han Qiu. 2024. An engorgio prompt makes large lan...

    2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.