Memory Extraction Techniques

• Memory extraction techniques are a suite of methods for isolating and reconstructing critical data from memory representations using statistical, machine learning, and hardware-based approaches.
• They employ high-precision classifiers, iterative cognitive loops, and side-channel attacks to achieve over 99% key recall and reduce search space by more than 90% in forensic applications.
• Integrating engineered metadata, statistical inversion, and proactive extraction methods enhances reliability and guides future research in secure, consistent memory acquisition across live systems.

Memory extraction techniques encompass a diverse set of methods and algorithms for isolating and reconstructing informative or sensitive content from memory representations. These techniques are foundational across fields such as digital forensics, security analysis, agent cognition, physical side-channel analysis, computational modeling of non-Markovian systems, and virtual machine introspection. The landscape spans high-level automated information extraction using machine learning, iterative cognitive approaches in artificial agents, rigorous statistical inversion in physical sciences, invasive hardware attacks, and methods for robust memory acquisition and analysis in the face of systemic inconsistencies. This article surveys principal methodologies, theoretical underpinnings, workflow design, and domain-specific best practices, drawing on recent advances from the research literature.

1. Memory Extraction in Digital Forensics and System Security

Within digital forensics, memory extraction focuses on identifying semantically meaningful data—primarily cryptographic material and credentials—directly from raw memory dumps. A key challenge is bridging the "semantic gap" between unstructured byte arrays and higher-level operating system or application constructs.

Contemporary memory extraction algorithms, exemplified by "SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap Dump" (Fellicious et al., 2022), frame the task on a per-window binary classification basis: given a heap dump $H$ of length $M$, split into overlapping windows $x_i\in\{0, \ldots, 255\}^{W}$ ($W=128$ bytes), the goal is to learn a classifier $f_\theta(x_i)\approx \Pr[y_i=1|x_i]$ to identify windows likely to contain SSH IV/key pairs. Preprocessing includes:

• Removal of all-zero or low-entropy pages using Hamming-distance filtering.
• Reshaping memory into aligned $N\times8$ matrices, computing first-order byte differences, and marking candidate blocks via difference patterns.

SmartKex uses a stacked ensemble of Random Forest classifiers, consisting of a high-precision branch (trained on naturally-imbalanced data), a high-recall branch (using SMOTE for rebalancing), and a meta-classifier operating on the probability outputs. This architecture achieves high throughput and a >90% search space reduction relative to naive brute-force, with $>$99% key recall on test sets. The extraction workflow combines window filtering via the classifier with a secondary brute-force decryption check to authenticate candidate keys (Fellicious et al., 2022).

Advancements in virtual machine introspection and forensic memory analysis leverage engineered features and supervised learning to automate structure recognition. Methods such as MetaKex, HeaderKex, and GraphKex compute statistical, allocation-header, and heap pointer-graph features respectively, each providing incremental improvements in identifying key or pointer blocks within heap or kernel memory (Fellicious et al., 7 Mar 2025). The adoption of additional context metadata (OS version, RAM size, hypervisor type) significantly enhances performance with limited data.

2. Proactive and Iterative Memory Extraction in Agent Architectures

Long-context LLM agents face the challenge of retaining and compiling relevant episodic content from extended dialogues. Traditional approaches rely on static, ahead-of-time summarization, which discards potentially important details not anticipated at extraction time. These limitations—summarization "blindness" and lack of extraction feedback—are formalized under Recurrent Processing Theory.

"Beyond Static Summarization: Proactive Memory Extraction for LLM Agents" (Yang et al., 8 Jan 2026) introduces ProMem, which frames extraction as a recurrent, iterative verification loop. The agent first produces a rough summary $M_0=\mathrm{LLM}(D; P_\mathrm{extract})$, then recovers missing or incorrect facts through cycles:

1. Generate self-directed questions based on current memory $M_t$.
2. Search dialogue history $D=\{t_1,\ldots,t_n\}$ for evidence answering these questions.
3. Update memory $M_{t+1}$ by replacing or pruning candidate facts based on answer support and deduplication.

Cosine-similarity embedding thresholds ($\tau_\mathrm{match},\tau_\mathrm{sim}$) gate both memory completion and deduplication. The process yields higher memory integrity and downstream QA accuracy than prior summarization techniques, along with favorable token cost–quality tradeoffs (e.g., ProMem achieves $73.8\%$ memory integrity and $62.26\%$ QA accuracy on HaluMem, outperforming static extraction baselines) (Yang et al., 8 Jan 2026).

3. Extraction of Physical or Cryptographic Secrets from Hardware

On-die secrets in non-volatile memory—such as antifuse-based OTP or battery-backed register arrays—are targets for both passive and active hardware memory extraction attacks.

Focused ion beam (FIB) passive voltage contrast (PVC) enables the recovery of bits stored in antifuse arrays by exploiting the voltage dependence of secondary electron imaging. In the "Extraction of Secrets from 40nm CMOS Gate Dielectric Breakdown Antifuses by FIB Passive Voltage Contrast" method (Zonenberg et al., 22 Jan 2025):

• Ga+ ion beam irradiation at 30 keV injects charge into exposed Metal-1 contacts.
• The contact node voltage under positive charge injection depends on the programmed state (conductive filament present or absent), resulting in strong contrast in secondary electron yield ($\sigma(V)=\sigma_0\exp(-\alpha V)$).
• Bit-by-bit readout yields the logical OR of paired memory cells, but optimized approaches (selective word-line activation, FIB metal deposition) can deconvolve the pair and extract true bit values.

Extraction timescales are practical (tens of kilobits per day), with negligible bit error rates ($<10^{-6}$) (Zonenberg et al., 22 Jan 2025).

Laser-assisted side-channel analysis automates secret bit extraction even on unknown IC layouts. High-resolution thermal laser stimulation (TLS) and logic-state imaging (LLSI) techniques yield 2D leakage maps, which are processed by CNN-based classifiers trained on image–key pairs. Successful key reconstruction at 100% bitwise accuracy is demonstrated on BBRAM, SRAM, and FPGA register targets; the attack is technology-agnostic and fundamentally layout-independent (Krachenfels et al., 2021).

4. Statistical Inference and Memory Kernel Extraction in Physical Modeling

In molecular dynamics and condensed matter, memory kernel extraction quantifies non-Markovian friction from projected, low-dimensional observables. The procedure addresses the generalized Langevin equation (GLE):

$$m\ddot x(t) = -\frac{dU[x(t)]}{dx} - \int_0^t K(t-s)\dot x(s) ds + F_R(t)$$

where $K(t)$ is the memory kernel and $U(x)$ the potential of mean force. Two primary approaches are employed (Tepper et al., 2024):

• Direct Volterra Inversion (for $\Delta t < \tau_\mathrm{mem}$): Discretizing the Volterra equation for running memory integral $G_n$, differentiating to recover $K_n$.
• Gaussian Process Optimization (GPO, for $\Delta t > \tau_\mathrm{mem}$): Adopting a parametric sum-of-exponentials for $K(t)$, optimizing kernel parameters to minimize the discrepancy between empirical and GLE-simulated autocorrelation functions, using a Bayesian acquisition strategy.

Direct inversion is robust when time resolution matches intrinsic memory, while GPO extends applicability to sparse or mesoscopic data, provided the discretization is finer than the slowest kinetic process (Tepper et al., 2024).

5. Extraction from Encrypted and Virtualized Memory

In the context of memory encryption (e.g., AMD SEV-protected virtual machines), memory extraction must circumvent the veiling of contents by keys inaccessible to an attacker. The SEVered family of attacks exploits the absence of integrity protection in hardware-encrypted VMs.

The adversary operates in two main phases (Morbitzer et al., 2019):

1. Observational Inference: The hypervisor tracks guest page accesses and IO events (e.g., TLS handshakes, disk writes) via SLAT page faults to infer a shortlist of recently accessed guest-physical pages likely to contain secrets.
2. Targeted Extraction and Analysis: Pages are selectively remapped and exfiltrated using SEVered mechanisms. Extracted contents are analyzed on-the-fly using entropy heuristics and domain-specific fingerprint tests (e.g., factorizing RSA keys, identifying kernel key schedules) until the target material is found.

This pipeline attains highly efficient secret recovery (tens of seconds for typical searches) with negligible forensic footprint and $>99\%$ success probability (Morbitzer et al., 2019).

6. Consistency Analysis and Reliability in Memory Dumps

Volatile memory acquisition for forensic analysis is inherently susceptible to inconsistencies when performed on live systems, due to concurrent kernel and user-space activity. The "A Comprehensive Quantification of Inconsistencies in Memory Dumps" study (Oliveri et al., 19 Mar 2025) instrumented QEMU via PANDA to record and analyze all kernel writes during memory acquisition.

Key findings include:

• All common acquisition tools follow an ascending-physical-page order, without quiescing the system.
• Inconsistencies appear as causal (alloc/free reordering, pointer redirection) and value (content drift) errors, quantified by the inconsistency ratio $R = N_\mathrm{inconsistent} / N_\mathrm{total}$ (e.g., $26.7\%$ for kernel pointers on Windows).
• Factors such as file system type, I/O mode, and acquisition method significantly modulate kernel write rates and dump time, with in-kernel tools and buffered I/O providing the best practical trade-off.

High inconsistency rates degrade the reliability of memory forensics tools (e.g., Volatility pointer-walk plugins) and lead to silent omission or misclassification of forensic artifacts. Carving-based approaches (structure recovery directly from memory substrings) and cross-validation of kernel object graphs are recommended for mitigating, though not eliminating, these errors (Oliveri et al., 19 Mar 2025).

7. Practical Guidance, Dataset Availability, and Future Prospects

A significant trend across memory extraction techniques is the systematic adoption of engineered features, machine learning, and statistically principled inference to increase accuracy, throughput, and automation. Major recent datasets (e.g., 1.5 TB of memory dumps and 15,000+ OpenSSH process heaps (Fellicious et al., 7 Mar 2025)) underpin reproducible research and tool benchmarking.

Best practices for research and operational deployment include:

• Leveraging a priori metadata and allocation-structure information wherever possible to bridge the semantic gap.
• Employing pointer-graph representation and complex feature sets for maximal performance in structure extraction.
• Using cross-validation and ablation studies to quantify the benefit of metadata or feature engineering.
• Carefully selecting acquisition tools and parameters to balance reliability, atomicity, and dump time.

Open challenges persist in the atomic and causally consistent acquisition of live memory, robust demultiplexing of physical-layer secret leakage, and the integration of learning-based extraction tools into operational forensic and agent stacks.

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References:

• (Fellicious et al., 2022): SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap Dump
• (Yang et al., 8 Jan 2026): Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
• (Fellicious et al., 7 Mar 2025): Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis
• (Oliveri et al., 19 Mar 2025): A Comprehensive Quantification of Inconsistencies in Memory Dumps
• (Zonenberg et al., 22 Jan 2025): Extraction of Secrets from 40nm CMOS Gate Dielectric Breakdown Antifuses by FIB Passive Voltage Contrast
• (Krachenfels et al., 2021): Automatic Extraction of Secrets from the Transistor Jungle using Laser-Assisted Side-Channel Attacks
• (Tepper et al., 2024): Accurate Memory Kernel Extraction from Discretized Time Series Data
• (Morbitzer et al., 2019): Extracting Secrets from Encrypted Virtual Machines