Extractable Memorization in LLMs

Updated 17 February 2026

Extractable memorization is the phenomenon where LLMs reproduce exact training data sequences when triggered by specific or adversarial prompts.
It is characterized by diverse taxonomies and measurement methodologies, including prefix-based, probabilistic, and entity-level extraction techniques.
Mitigation strategies such as data deduplication, prompt engineering, and architectural safeguards are essential to reduce privacy and copyright risks.

Extractable memorization denotes the subset of a model's memorized training data that can be retrieved at inference time using carefully crafted or even adversarial queries. Unlike general overfitting or latent storage, extractable memorization is operationalized in terms of observable string emission—such as verbatim regurgitation of training sequences, sensitive entities, or high-fidelity reproductions beyond what one would expect from natural language modeling. This phenomenon is particularly germane in the context of LLMs, where the scale, diversity, and sensitivity of training corpora, coupled with generative flexibility, amplify both the risk and practical consequences of data leakage.

1. Formal Definitions and Taxonomies

Extractable memorization is operationalized as the generation, by a trained model, of verbatim or near-verbatim substrings from its training corpus in response to input prompts chosen by an external actor, often without direct access to the training set (Nasr et al., 2023, Ma et al., 17 Apr 2025, Raunak et al., 2022). The central mathematical form is: for training corpus $\mathcal{D}$ , a model's generation function $\textsf{Gen}$ , and a sequence $x\in\mathcal{D}$ , extractable memorization holds if

$\exists p\quad \text{s.t.} \quad \textsf{Gen}(p) = x.$

Several concrete definitions have been used:

Prefix-based: A sequence is extractably memorized if, when provided with a ground-truth or adversarially discovered prefix $p$ , the model's completion matches the suffix exactly (Hayes et al., 2024, Ma et al., 17 Apr 2025, Yang et al., 2023).
Probabilistic extraction: $(n,p)$ -discoverable memorization rates are defined as the probability of recovering a suffix in $n$ independent sampling runs exceeding a given threshold $p$ (Hayes et al., 2024).
Entity-level and partial context: Memorization is flagged if, given a subset of known entities (attributes) from a record, the model's continuations include the correct withheld entity (Zhou et al., 2023).
Multi-prefix robustness: A sequence is only considered robustly memorized if it can be elicited via a multiplicity of syntactically or semantically diverse prefixes (Dang et al., 25 Nov 2025).
Adversarial black-box extraction: A substring $s$ (e.g., 50 tokens) from any point in a generated output is counted if $s\in\mathcal{D}$ , regardless of prefix (Nasr et al., 2023).

Taxonomies now distinguish between direct recall (high duplication, likely verbatim), guess (highly predictable or low-complexity completions), and non-memorized/novel content (Dentan et al., 4 Aug 2025). Entity-level definitions increase fidelity to privacy exposures in real deployments (Zhou et al., 2023).

2. Quantification and Methodologies

Diverse methodologies have developed to measure, localize, and characterize extractable memorization:

Discoverable extraction tasks split training examples into (prefix, suffix) and attempt to recover the suffix with greedy or stochastic decoding (Hayes et al., 2024, Wang et al., 2024).
Prefix-prompting extraction slides a fixed-length window to reconstruct long-form sequences or books from a short initial prefix, scoring fidelity with metrics such as Jaccard, BLEU, and ROUGE-L (Ma et al., 17 Apr 2025).
Monte Carlo (probabilistic) estimation: For a given $(p,s)$ pair, estimate the empirical fraction $\widehat{P}_{\text{extract}}$ of completions matching $s$ , and model the aggregate risk across plausible adversary budgets ( $n$ queries) (Hayes et al., 2024).
Membership inference and perplexity-ratio scoring: Classifies outputs as memorized based on low sequence perplexity, or outlier status in loss/entropy space (Yang et al., 2023, Nasr et al., 2023).
Gradient-based adversarial search: Finds multiple distinct prefixes that robustly elicit target memorized sequences, substantiating the "robust memory basin" hypothesis (Dang et al., 25 Nov 2025).
Attention and activation analyses: Trains CNNs or probes layerwise representations to distinguish memorized, guessed, and novel outputs, and attribute recall to network submodules (Dentan et al., 4 Aug 2025, Haviv et al., 2022).

Key metrics include memorization rate, exact extraction rate, top-k recall, and sequence coverage, for both verbatim and entity-level extractability.

3. Scaling Laws, Architectural Drivers, and Thresholds

Model capacity, training dynamics, and architectural choices induce sharp phase transitions in extractable memorization:

Capacity threshold effect: In controlled synthetic tasks, Barron & White demonstrate a step-function relationship where below a critical parameter count ( $\sim$ 6k for their setup), almost no facts are memorized, but above threshold, recall becomes perfect (Barron et al., 10 Jun 2025).
BPE vocabulary size: Larger subword vocabularies reduce input sequence length and concentrate memorization in feed-forward layers, facilitating extraction (Kharitonov et al., 2021).
Repetition and duplication: Strings appearing more times in the training corpus are dramatically more likely to be extractably memorized (Yang et al., 2023, Zhou et al., 2023).
Model scaling and dataset size: Larger models and longer training generally increase both the absolute number and variety of extractable sequences (Nasr et al., 2023, Ma et al., 17 Apr 2025).
Positional fragility: Memorization is most extractable when prefixes are drawn from the earliest tokens of a sequence; offsetting the prompt into the context window sharply suppresses recall (Xu et al., 19 May 2025).
Alignment and mitigation: RLHF or instruction tuning hides but does not erase base-LM memorization, which can resurface with adversarial prompt engineering (Nasr et al., 2023, Ma et al., 17 Apr 2025).

4. Empirical Findings and Limitations

Large language and code models exhibit substantial extractable memorization under black-box and white-box querying:

Open-source LLMs: Adversarial attacks on GPT-Neo, Pythia, LLaMA, Falcon, and similar architectures recover hundreds of thousands to millions of training substrings, with token-level memorization rates from 0.1% up to 1.4% (Nasr et al., 2023).
Production-aligned models: Alignment suppresses direct regurgitation under normal interaction, but simple repeated-token prompts ("divergence attacks") awaken base-LM behaviors that leak data at up to 150x the normal rate (Nasr et al., 2023). Fine-tuning can undo or reintroduce suppression of memorization with only minor weight changes concentrated in the lowest layers (Ma et al., 17 Apr 2025).
Entity and attribute-level attacks: Even supplying only partial identifying context, modern LLMs recover sensitive entities with high probability, especially for records duplicated in training data (Zhou et al., 2023).
Code and algorithmic models: Memorization is especially pronounced in code LMs, with repeated license blocks, typical boilerplate, and low-TTR strings dominating extracted content (Yang et al., 2023).
Quantitative extraction efficiency: Dynamic, prefix-dependent soft prompts and multi-query probabilistic metrics reveal up to 100% more extractable memorized content than naive, prefix-only extraction (Wang et al., 2024, Hayes et al., 2024).

5. Mechanisms and Internal Representations

Internal analysis of transformer architectures reveals that:

Layerwise specialization: Early layers perform rapid “candidate promotion”—sharply reducing the rank of memorized tokens in the output distribution—while later layers drive up model confidence and reinforce output probability (Haviv et al., 2022, Dentan et al., 4 Aug 2025).
Attention block roles: Syntactic guessing is associated with mid-layer diagonal attention, while memorized recall via high duplication is encoded in deeper layers just below the diagonal (Dentan et al., 4 Aug 2025).
Hybrid architectures for transparency: Explicit associative memory models such as MeMo make stored associations enumerable and deletable, enabling direct auditing and right-to-be-forgotten operations, contrasting with the distributed, opaque weights of standard transformers (Zanzotto et al., 18 Feb 2025).
Isolation mechanisms: Training architectures that explicitly separate shared “generalization” components from sequence-tied “memorization sinks” can localize storage and allow post-hoc removal of memorized items without degrading general performance (Ghosal et al., 14 Jul 2025).

6. Mitigation, Auditing, and Policy Considerations

Multiple strategies and auditing recommendations have been proposed:

Data deduplication: Removing repeated or near-duplicate records prior to training steeply lowers extractable memorization rates (Yang et al., 2023, Zhou et al., 2023).
Probabilistic auditing: Reporting $(n,p)$ -curves for extractability, rather than single binary rates, provides a richer risk profile reflective of realistic adversaries (Hayes et al., 2024).
Prefix-offset controls: Shifting sensitive data to deeper locations in the context window suppresses recall and reduces degenerative output side-effects (Xu et al., 19 May 2025).
Train-time isolation and model editing: Isolation of memorization by design, e.g., via MemSink neurons or associative memory modules, permits post-hoc removal mechanisms (Ghosal et al., 14 Jul 2025, Zanzotto et al., 18 Feb 2025).
Soft-prompt-based suppression: Defensive continuous prompts or prefix-tuning can suppress the emission of memorized content even if it remains represented internally (Wang et al., 2024).
Automatic detection and early prediction: Techniques based on pointwise mutual information of intermediate representations enable prediction (and optional removal) of at-risk samples during or prior to training (Dentan et al., 2024).
Rate-limiting and runtime access control: API-level restrictions on sampling parameters (temperature, top-k) and query budgets can contain extraction risk (Hayes et al., 2024).
Compositional and policy-based governance: Attribution, consent, and data-provenance tracking, coupled with multidisciplinary compliance, are essential for operational deployment (Yang et al., 2023).

7. Open Questions, Limitations, and Future Directions

Despite rapid methodological advances, several open fronts remain:

Separation of memorization from generalization: Capacity limits and joint-task studies indicate that beyond a sharp parameter threshold, models abandon simple rules for verbatim storage, collapsing extrapolative generalization (Barron et al., 10 Jun 2025). Hybrid modular architectures may be needed to maintain both.
False positives and robustness: Single-path extraction tests can conflate high-probability, compositional completions with true memorization. Multi-prefix or adversarial search reduces but does not erase ambiguity (Dang et al., 25 Nov 2025, Dentan et al., 4 Aug 2025).
Alignment and post-training defense: Alignment can mask, but not remove, memorized content; fine-tuning can swiftly re-enable regurgitation (Ma et al., 17 Apr 2025, Nasr et al., 2023). Whether provably robust alignment is achievable is unclear.
Scalability and completeness: Enumeration of all extractable content remains infeasible at trillion-parameter/data scales; even extrapolative estimation with Good–Turing methods is only approximate (Nasr et al., 2023).
Audit and compliance mechanisms: Integration of in-model auditing, data watermarking, and runtime output scoring is emergent but lacks standardization (Yang et al., 2023).
Transfer domain and transferability: Most methodologies, metrics, and defenses have been benchmarked on English text and code; broader evaluations on multilingual, cross-domain, or multimodal data are in early stages.

Extractable memorization thus remains a critical, multidimensional challenge in contemporary language modeling—defining the actionable boundary between benign retention, functional recall, and practical privacy/copyright risk. Ongoing research intertwines empirical auditing, architectural innovation, policy measures, and theoretical analysis to narrow the gap between observable extraction and latent storage, ensuring models can be trained, deployed, and controlled with precision and accountability.