Pith. sign in

REVIEW 4 major objections 5 minor 2 cited by

Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Multi-stage prompt inference attacks can extract confidential context from enterprise LLMs even when each individual prompt looks benign.

desk verdict Real problem, missing evidence: the paper's load-bearing attack and defense numbers are unreported, the formal results are definitional or absent, and the citation list does not line up; not ready for referees. read the letter →

arxiv 2507.15613 v1 pith:BLZQ4HHB submitted 2025-07-21 cs.CR cs.AI

Andrii Balashov , Olena Ponomarova , Xiaohua Zhai This is my paper
classification cs.CRcs.AI
keywords multi-stagepromptinferenceenterpriseLLMsecurityinjectioninformationleakagedifferentialprivacyanomalydetectionspotlightingretrieval-augmentedgeneration
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 seeks to establish that multi-stage prompt inference attacks, where an adversary chains many innocuous-looking queries to gradually pry secrets from an LLM's context, can defeat standard single-turn safeguards and must be treated as a first-class enterprise threat. The authors formalize the attack as a sequential information-gathering process and report simulated exfiltrations, including reconstructing a 500-word confidential report with 90% accuracy over 20 dialogue turns and extracting an $n$-bit secret with over 99% success using about $n$ binary-search questions. They then argue that the defense must move beyond per-turn filtering to conversation-level monitoring, context isolation, and access control, and they present layered defenses with claimed results such as an anomaly detector at AUROC 0.95 and spotlighting reducing attack success from over 50% to under 2%. If the central claim is right, organizations running LLMs over private email, documents, or knowledge bases cannot rely on refusal behavior or single-prompt filters to protect confidential data.

What carries the argument

The central object is the per-query information leakage $\ell = I(S; O_t | O_{1:t-1})$, the number of bits one question-answer round can be forced to reveal about the secret. Treating the LLM as a communication channel from the secret to the attacker, the paper derives that total leakage over $T$ turns is bounded by $T\ell$, which inverts to the query count $T \geq H(S)/\ell$; it then invokes differential privacy to argue that per-query leakage is $O(\varepsilon)$, a step the text itself calls an oversimplification and that lacks a promised derivation. The attack strategies are carried by adaptive questioning, including binary search on secret bits, masked-completion queries that fill in missing words, and hidden instructions embedded in retrieved content; the main defensive mechanism is spotlighting, which wraps untrusted input in delimiters so the model treats it as data rather than as instructions.

What would settle it

Run the paper's headline scenario: an adaptive attacker with 20 turns against an enterprise-style retrieval-augmented assistant with standard content filters and a prompt-injection classifier, targeting a 500-word confidential report, and record how much of the report is reconstructed; the central attack claim fails if accuracy falls far below 90%. Separately, inspect the promised appendix: the paper asserts an $\varepsilon$-DP bound on per-query mutual information and calls it an oversimplification, but no derivation appears in the manuscript.

Watch

Extended reading notes

Core claim

The central claim is that an attacker who can hold a multi-turn conversation with an LLM that has retrieval access to private corporate data can reconstruct sensitive documents even when the model is instructed not to disclose them and each individual prompt would pass a content filter or injection classifier. The paper models the secret as a random variable $S$, the attacker's queries as adaptive choices that maximize the conditional mutual information $I(S; O_t | O_{1:t-1})$, and the total leakage after $T$ turns as at most $T\ell$; an attacker therefore needs roughly $H(S)/\ell$ turns for full extraction. Its reported simulations include an EchoLeak-style indirect injection through a hidden instruction embedded in an email, an adaptive yes/no search that exfiltrates an $n$-bit secret with over 99% success using about $n$ questions, and a case where a 500-word report is reconstructed at 90% accuracy in 20 turns. The paper also claims that defenses, including statistical anomaly detection, fine-grained access control, prompt sanitization, spotlighting, a watchdog output filter, and differential privacy training, reduce or bound leakage, and that the combined defense-in-depth strategy prevents complete exfiltration in more than 95% of attempted attack dialogues.

Load-bearing premise

The results stand or fall on the premise that each question-answer round leaks a well-defined, bounded amount of information about the secret and that differential privacy shrinks that per-round leakage predictably; the text itself flags a supporting claim as an oversimplification and its promised derivation is absent.

Editorial extensions

If this is right

  • Enterprise LLM assistants would need conversation-level monitoring, scoring whole multi-turn sessions rather than individual prompts, as a necessary control.
  • If per-query leakage is bounded by $\ell$, capping the number of turns a user can hold with sensitive context directly caps total leakage at $T\ell$.
  • Separating untrusted external content from internal data in the prompt, via spotlighting or similar context isolation, should become a default architecture for retrieval-augmented systems.
  • If the differential privacy bound holds, DP training can convert a model that would leak a secret in one turn into one that leaks only a slow trickle, making DP relevant for high-sensitivity enterprise fine-tuning despite its utility cost.
  • The reported results imply that current content filters and injection classifiers are insufficient on their own and that defenses must be benchmarked over multi-turn attack chains.

Reading between the lines

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

  • The paper's own bound suggests a sharper empirical test than it reports: measuring $\ell$ per query on a deployed assistant would let an organization compute, from the entropy $H(S)$ of a target secret, a hard upper bound on how many turns any user should be allowed before remediation.
  • The missing derivation behind the claim that an $\varepsilon$-DP model has per-query leakage $I(S; O_t) \leq \varepsilon$ means the DP defense should be read as conditional on a nontrivial channel-capacity argument rather than as established.
  • Because the reported attack simulations are not reproducible from the manuscript, the headline numbers (90% reconstruction in 20 turns, 99% success for binary search) are not yet independently checkable; a public benchmark with transcripts would settle them.
  • The same information-theoretic framing could be inverted into an auditing tool: plant decoy secrets with known entropy, monitor leakage traces, and estimate whether a deployed system is currently leaking at a detectable rate.
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 / 5 minor

Summary. The paper studies multi-stage prompt inference attacks against enterprise RAG-based LLM assistants: an adversary chains seemingly benign prompts to gradually extract confidential data from an LLM's context. It proposes a threat model, describes attack strategies (indirect injection, adaptive questioning, output encoding), offers an information-theoretic analysis of cumulative leakage, and advocates a defense-in-depth stack (anomaly detection, fine-grained access control, prompt sanitization/spotlighting, DP training, and architectural changes). The central empirical claim is that such attacks reliably exfiltrate sensitive enterprise data even when standard safety measures are in place, with reported figures such as 90% reconstruction of a 500-word report in 20 dialogue turns, AUROC 0.95 for detection, and over 95% prevention by the combined defense. The manuscript presents these as results of simulations, but no experimental protocol, dataset, model, or code is included.

Significance. The problem is timely and important: if substantiated, the paper would support the practical conclusion that single-turn prompt filtering is insufficient and that enterprise LLM deployments need conversation-level monitoring and context isolation. The manuscript also makes a useful conceptual contribution by framing multi-stage inference as a sequential decision problem and enumerating concrete defense layers. However, as submitted, the significance cannot be assessed because the reported attack and defense numbers have no reproducible experimental basis, the formal analysis is largely definitional, and the appendix that would support the channel-capacity and DP arguments is absent. The contribution is therefore a research agenda rather than a validated set of findings.

major comments (4)
  1. [§2.3, §3.1, §4.5] The empirical core of the paper is not reproducible: the manuscript never specifies the LLM(s), versions, retrieval corpus, prompt templates, refusal configuration, permission boundary, detector features, or train/test split behind the reported numbers (90% reconstruction of a 500-word report in 20 turns, AUROC 0.95, 88% token flagging, >95% prevention). This is load-bearing because the abstract's central claim that multi-stage attacks 'reliably exfiltrate sensitive information' rests entirely on these simulations. The inconsistency between the claimed AUROC 0.95 vs baseline 0.82 (§1) and Table 1's TPR values (0.90 at 90% TNR, 0.75 at 95% TNR) further prevents checking. Without a full experimental protocol, the attack and defense results cannot be verified or compared.
  2. [§3.2] The formal leakage bound is a restatement of the definition of per-query leakage: I(S;O1:T)=Σ I(S;Ot|O1:t-1) is the chain rule, and T≥H(S)/ℓ follows directly from assuming each step leaks at most ℓ bits. The paper does not show that a refuse-capable RAG system has ℓ>0, nor how to estimate ℓ for any concrete model; thus the bound has no independent predictive content. The channel-capacity analysis that would give content is deferred to 'Appendix A', but no Appendix A appears in the manuscript.
  3. [§3.2 (DP claim), §6] The claim that 'for an ε-DP model, I(S;Ot) ≤ ε' is stated without proof and is immediately labeled an oversimplification; the promised derivation in Appendix A is absent. Standard DP gives a ratio bound on output probabilities, not a direct mutual-information bound of the form I(S;Ot)≤ε without additional assumptions. This matters because Figure 1, the 'DP blocks >95%' statement in §6, and the exponential-query conclusion in §4.4 all rely on this unproved bound. The manuscript needs either a correct, stated-theorem derivation or a withdrawal of the quantitative DP-defense claims.
  4. [§4.1, §4.4, §6] The quantitative defense claims are internally inconsistent: §4.1 reports FocusTrack 'achieved an AUROC of 0.95' and §4.5 says '96% detection rate at 5% false positive rate', whereas Table 1 gives TPR 0.75 at 95% TNR and does not report AUROC; §4.4 reports membership-inference precision 90% for a non-DP model and near chance for DP without any dataset, model size description, or attack methodology; §6 reports >95% prevention of complete exfiltration with no supporting protocol. Because these defense results are presented as the main practical contribution, they need a coherent, specified evaluation before the paper's conclusions can be credited.
minor comments (5)
  1. [References] In-text citations are frequently mismatched with the reference list: [20] is listed as Kirchenbauer et al.'s watermark paper, but the text attributes the spotlighting method to Hines et al. [20] (§1, §4.2); Hung et al.'s AttentionTracker is cited as [22], but reference [22] is Wang et al.'s Injecguard. The reference list needs systematic reconciliation.
  2. [§4.1] The name 'FocusTrack' is used in Table 1 and in the introduction but the method itself is never precisely defined; the reader cannot tell which of the listed features constitute FocusTrack as opposed to the baseline.
  3. [Reference [4]] The entry 'OW ASP Foundation' should be 'OWASP Foundation'.
  4. [§3.1] Figure 1 is explicitly labeled 'hypothetical' in the text, yet it is used in the abstract-level narrative as evidence of cumulative leakage and of DP slowing an attack; it should be clearly marked as an illustrative sketch, not an experimental result.
  5. [§2.2] The notation for the per-query leakage bound is inconsistent: the contributions list says 'at least b bits' while §2.2 and §3.2 use ℓ (and at one point L). The same symbol should be used throughout, with a clear definition.

Circularity Check

1 steps flagged · score 1.0 of 10

No significant circularity: the sole formal bound is definitional, while the empirical core is unreproducible but not circular.

  1. self definitional [Section 3.2, information-theoretic leakage analysis (equations after 'per-query leakage')]
    "Suppose each query/response pair leaks at most L bits on average. Formally, let It = I(S; Ot | O1:t−1) be the conditional information gain at step t. Then I(S; O1:T ) = PT t=1 I(S; Ot | O<t) = PT t=1 It. If It ≤ ℓ for all t (or on average E[It] ≤ ℓ), then I(S; O1:T ) ≤ T ℓ. Inverting, to leak H(S) bits, one needs T ≥ H(S)/ℓ queries."

    The derived bound T ≥ H(S)/ℓ is a direct rearrangement of the chain rule for mutual information once ℓ is defined as the per-query conditional information gain. No independent measurement or fitted value of ℓ is supplied, so the bound carries no predictive content beyond the definition of ℓ itself. However, this definitional step is not load-bearing for the paper's headline empirical claims, which come from unstated simulations, so it contributes only minor self-definitional circularity.

full rationale

The paper's central derivation chain is largely non-circular. The attack-feasibility claim rests on simulated scenarios (Sections 2.3 and 3.1) and the defense claims on synthesized evaluations (Tables 1-2 and Sections 4.3-4.5), none of which are parameterized by the formal model. The formal model in Section 3.2 is an information-theoretic identity: it defines per-query leakage via conditional mutual information and then states T ≥ H(S)/ℓ, which is a rearrangement of the chain rule. This is tautological but not an empirical prediction, and it is not used to fit any experimental number. The differential-privacy bound is imported from an external standard result [42], explicitly called an oversimplification, and the promised Appendix A is absent; these are rigor and reproducibility gaps, not circularity. No load-bearing self-citation appears; references [20], [22], and [42] are external prior work. The headline 90%-in-20-turns and AUROC figures are unsupported by protocol, but circularity analysis cannot reach that issue. The low score reflects only the definitional formal bound; there is no fitted-input-as-prediction pattern and no self-citation chain forcing the results.

Assumptions & free parameters 0 free parameters · 5 assumptions · 1 invented entities

The formal machinery rests on standard information-theoretic identities plus unproved domain assumptions about LLM behavior and DP training. The empirical claims are unsupported because no data, code, or experimental protocol is provided, so the paper's new content cannot be independently checked.

assumptions (5)
  • domain assumption LLM cannot reliably distinguish user instructions from retrieved data when not explicitly delimited.
    Central to attack feasibility; stated in Section 2.1: 'The LLM cannot inherently distinguish which parts of its input are user instructions versus retrieved reference data.'
  • domain assumption Enterprise LLM follows RAG with retrieval constrained by user permissions.
    System model in Section 2.1; if retrieval did not expose sensitive context, many described attacks would not apply.
  • ad hoc to paper For an ε-DP model, per-query mutual information I(S;Ot) is O(ε).
    Invoked in Section 3.2 to bound leakage and justify the DP defense; the paper calls it an oversimplification and provides no proof.
  • ad hoc to paper If the LLM is constrained to output only summaries of authorized data, mutual information between unauthorized data and output is bounded by a small δ.
    Section 4.2 claims a formal safety guarantee but gives no proof or definition of δ.
  • domain assumption Attacker can choose queries that force roughly one bit of secret per response, forming a bit-extraction oracle.
    Supports the binary-search case study in Sections 2.3 and 4.5; no evidence is given that the model complies at this rate.
invented entities (1)
  • FocusTrack anomaly detector
    purpose: Detect multi-turn prompt inference attacks from conversation features.
    Named system described in Section 4.1 with a reported AUROC of 0.95, but no implementation, dataset, or independent evaluation is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems." pith.science (2026). https://pith.science/paper/BLZQ4HHB

@misc{pith2026250715613,
  author       = {Pith},
  title        = {Pith review of: Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BLZQ4HHB}},
  note         = {Machine review of arXiv:2507.15613}
}
read the original abstract

Large Language Models (LLMs) deployed in enterprise settings (e.g., as Microsoft 365 Copilot) face novel security challenges. One critical threat is prompt inference attacks: adversaries chain together seemingly benign prompts to gradually extract confidential data. In this paper, we present a comprehensive study of multi-stage prompt inference attacks in an enterprise LLM context. We simulate realistic attack scenarios where an attacker uses mild-mannered queries and indirect prompt injections to exploit an LLM integrated with private corporate data. We develop a formal threat model for these multi-turn inference attacks and analyze them using probability theory, optimization frameworks, and information-theoretic leakage bounds. The attacks are shown to reliably exfiltrate sensitive information from the LLM's context (e.g., internal SharePoint documents or emails), even when standard safety measures are in place. We propose and evaluate defenses to counter such attacks, including statistical anomaly detection, fine-grained access control, prompt sanitization techniques, and architectural modifications to LLM deployment. Each defense is supported by mathematical analysis or experimental simulation. For example, we derive bounds on information leakage under differential privacy-based training and demonstrate an anomaly detection method that flags multi-turn attacks with high AUC. We also introduce an approach called "spotlighting" that uses input transformations to isolate untrusted prompt content, reducing attack success by an order of magnitude. Finally, we provide a formal proof of concept and empirical validation for a combined defense-in-depth strategy. Our work highlights that securing LLMs in enterprise settings requires moving beyond single-turn prompt filtering toward a holistic, multi-stage perspective on both attacks and defenses.

Figures

Figures reproduced from arXiv: 2507.15613 by the authors.

Figure 1
Figure 1. Illustrative cumulative leakage as the number of attacker queries increases. The blue curve shows an [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents

    cs.CR 2026-06 unverdicted novelty 6.0 of 10

    Activation probes, calibrated honeytokens, and multi-turn leakage accounting detect credential exfiltration attempts in LLM agents with high accuracy in controlled open-model tests.

  2. Multimodal Prompt Injection Attacks: Risks and Defenses for Modern LLMs

    cs.CR 2025-09 conditional novelty 4.0 of 10

    Across four attack types, six of eight tested LLMs were successfully injected in at least one category, and GPT-4o failed every test.

Reference graph

Works this paper leans on

48 extracted references · 31 canonical work pages · cited by 2 Pith papers

  1. [20]

    Kirchenbauer et al

    J. Kirchenbauer et al. A watermark for large language models. arXiv:2301.10226, 2023

    arXiv 2023

  2. [22]

    Wang et al

    R. Wang et al. Injecguard: Benchmarking and mitigating over-defense in prompt injec- tion guardrail models. arXiv:2410.22770, 2024

    arXiv 2024

  3. [11]

    Hung et al

    K.-H. Hung et al. Attention tracker: Detecting prompt injection attacks in llms via distraction. Findings of NAACL , 2025

    work page 2025

  4. [1]

    Perez and I

    F. Perez and I. Ribeiro. Ignore previous prompt: Attack techniques for language models. NeurIPS ML Safety Workshop , 2022

    work page 2022

  5. [2]

    J. K. et al. (Carlini). Training data extraction from large language models: A survey. ACL Workshop on TrustNLP , 2023

    work page 2023

  6. [3]

    Greshake et al

    K. Greshake et al. More than you’ve asked for: A comprehensive analysis of novel prompt injection threats to application-integrated large language models.arXiv preprint arXiv:2302.12173, 2023. 23

    arXiv 2023

  7. [4]

    Owasp top 10 for large language model applications, 2023

    OW ASP Foundation. Owasp top 10 for large language model applications, 2023. https://owasp.org/www-project-top-10-for-large-language-model-applications/

    work page 2023

  8. [5]

    Carlini et al

    N. Carlini et al. Extracting training data from large language models. USENIX Security, 2021

    work page 2021

Show all 48 references
  1. [6]

    Shokri et al

    R. Shokri et al. Membership inference attacks against machine learning models. IEEE SP, 2017

    2017

  2. [7]

    Kaddour et al

    J. Kaddour et al. Challenges and applications of large language models. arXiv:2307.10169, 2023

    2023 arXiv

  3. [8]

    Croisdale

    L. Croisdale. Echoleak in microsoft copilot: What it means for ai security. Varonis Blog, June 2025

    2025

  4. [9]

    Salem et al

    A. Salem et al. BaFFLE: Backdoor analysis of fine-tuned language models. arXiv:2310.09756, 2023

    2023 arXiv

  5. [10]

    Gpt-4 system card, March 2023

    OpenAI. Gpt-4 system card, March 2023. https://cdn.openai.com/papers/GPT-4- System-Card.pdf

    2023

  6. [12]

    Tram` er et al

    F. Tram` er et al. Stealing machine learning models via prediction APIs. USENIX Security, 2016

    2016

  7. [13]

    Suo et al

    X. Suo et al. Signed-prompt: A new approach to prevent prompt injection attacks in llm-integrated applications. TechRxiv preprint, 2024

    2024

  8. [14]

    Bai et al

    J. Bai et al. Constitutional ai: Harmlessness from ai feedback. arXiv:2212.08073, 2022

    2022 arXiv

  9. [15]

    Perez et al

    S. Perez et al. Red teaming language models with language models. arXiv:2209.07858, 2022

    2022 arXiv

  10. [16]

    M. Harang. Prompt injection is a serious threat to LLM-based systems. NCSC-UK Publication, 2023

    2023

  11. [17]

    Liu et al

    X. Liu et al. Automatic and universal prompt injection attacks against LLMs. arXiv:2403.04957, 2024

    2024 arXiv

  12. [18]

    Yang et al

    Z. Yang et al. Llm-as-optimizer: Unleashing zero-shot coherent optimization capability of large language models. arXiv:2307.06449, 2023

    2023 arXiv

  13. [19]

    Hu et al

    Y. Hu et al. Membership inference attacks on machine learning: A survey. ACM CSUR, 54(11), 2022

    2022

  14. [21]

    He et al

    P.-S. He et al. Deberta v3: Improving deberta using electrastyle pre-training with gradient-disentangled embedding sharing. arXiv:2111.09543, 2021. 24

    2021 arXiv

  15. [23]

    Zunger et al

    Y. Zunger et al. Promptguard: Structured input tokens to detect prompt injections. arXiv preprint, Meta AI , 2024

    2024

  16. [24]

    Yi et al

    X. Yi et al. BIPIA: A dataset for prompt injection attacks. ACL Workshop, 2023

    2023

  17. [25]

    Chen et al

    J. Chen et al. Lessons from defending gemini against indirect prompt injections. Google AI Blog, 2024

    2024

  18. [26]

    Abdelnabi et al

    S. Abdelnabi et al. Task tracker: Probing apps via indirect prompt injection. arXiv:2308.01217, 2023

    2023 arXiv

  19. [27]

    Pasquini et al

    A. Pasquini et al. Neuralexec: Executing malicious code via llms with no user input. OpenReview Preprint, 2024

    2024

  20. [28]

    Yan et al

    M. Yan et al. Cognitive overload: Jailbreaking large language models with overloaded logical thinking. arXiv:2311.09827, 2023

    2023 arXiv

  21. [29]

    Zhao et al

    H. Zhao et al. Prompt as triggers for backdoor attack: Vulnerability in large language models. arXiv:2309.07663, 2023

    2023 arXiv

  22. [30]

    Mei et al

    C. Mei et al. Autohijacker: Automatic vulnerability scanning for prompt injection in llm agents. OpenReview Preprint, 2024

    2024

  23. [31]

    Li et al

    X. Li et al. Differentially private fine-tuning of language models. Journal of Privacy and Confidentiality , 12(1), 2022

    2022

  24. [32]

    Abadi et al

    M. Abadi et al. Deep learning with differential privacy. ACM CCS, 2016

    2016

  25. [33]

    Fredrikson, S

    M. Fredrikson, S. Jha, and T. Ristenpart. Model inversion attacks that exploit confi- dence information and basic countermeasures. ACM CCS, 2015

    2015

  26. [34]

    A. Wei, N. Zou, and B. Zhu. Jailbroken: How does llm safety training fail? arXiv preprint arXiv:2307.02483, 2023

    2023 arXiv

  27. [35]

    Zou et al

    A. Zou et al. Universal and transferable adversarial attacks on aligned language models. arXiv preprint arXiv:2307.15043 , 2023

    2023 arXiv

  28. [36]

    Hines et al

    T. Hines et al. Defending against prompt injection attacks in llms by back-translation and reranking. arXiv preprint arXiv:2309.02533 , 2023

    2023 arXiv

  29. [37]

    Ouyang et al

    L. Ouyang et al. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 35 , pages 27730–27744, 2022

    2022

  30. [38]

    Robey, E

    A. Robey, E. A. et al. Smoothllm: Defending large language models against jailbreaking attacks. arXiv preprint arXiv:2310.03684 , 2023

    2023 arXiv

  31. [39]

    Deng et al

    P. Deng et al. Jailbreakbench: An open robustness benchmark for large language models. arXiv preprint arXiv:2312.00844 , 2023. 25

    2023 arXiv

  32. [40]

    Amodei et al

    D. Amodei et al. Concrete problems in ai safety. arXiv preprint arXiv:1606.06565, 2016

    2016 arXiv

  33. [41]

    Schick et al

    T. Schick et al. Toolformer: Language models that teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023

    2023 arXiv

  34. [42]

    Yu et al

    D. Yu et al. Differentially private fine-tuning of large language models. In International Conference on Learning Representations, 2022

    2022

  35. [43]

    Carlini, C

    N. Carlini, C. Liu, U. Erlingsson, J. Kos, and D. Song. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium, 2019

    2019

  36. [44]

    Christ, N

    M. Christ, N. G. et al. Undetectable watermarks for language models. In International Conference on Machine Learning , 2023

    2023

  37. [45]

    Qi et al

    X. Qi et al. Badllama: A stealthy and controllable backdoor attack against aligned llms. arXiv preprint arXiv:2404.14275 , 2024

    2024 arXiv

  38. [46]

    T. Shin, Y. Ribeiro, E. A. et al. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , pages 4222–4235, 2020

    2020

  39. [47]

    Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, and M. Wang. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2023

    2023 arXiv

  40. [48]

    Li et al

    X. Li et al. Large language models can be strong differentially private learners. In International Conference on Learning Representations , 2024. 26

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.