64 results for “file”

Turing LLM post-training combines calibrated multi-teacher on-policy distillation (C-MOPD) with an execution path for dynamically routed mixture-of-experts models. C-MOPD matches each teacher on a local Top-K support with teacher-wise loss normalization and clipping. On Turing-20B-A2B it scores 0.9988 on four-domain integration, beats Qwen3-8B on math and code (85.92 vs 80.33 mean), and cuts consolidation step time from 398.7 to 139.3 seconds in a PPU profile.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

This arXiv paper (October 2026) replicates Chen et al.'s study of LLM-based smart contract vulnerability detection. It confirms the high-recall, low-precision pattern on GPT-4, then tests Mixture-of-Experts and RNN-like models plus a preliminary State Space Model run. Gemma-3 matches GPT-4's profile despite far fewer parameters, while two Mixture-of-Experts models diverge, so architecture family alone does not predict detection behavior.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🛡️ Security

This paper measures job-based doubt in LLM fact-checkers: judging a claim false more often when the poster has a lower-status job. It compares base and post-trained checkpoints of six model families on fact-checked claims with randomised poster profiles. Only the open Tülu-3 recipe (OLMo-2 32B, Tülu-3 70B) creates the effect, under one percentage point but reliable. In OLMo-2 32B the flip comes from one residual-stream direction and appears at supervised fine-tuning, confirmed on 400 new claims. None of seven commercial models doubts lower-status posters more.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

Poly2MsgLeaks is a profiled chosen-ciphertext power-analysis attack on the SMAUG-T (TiMER parameter set) post-quantum KEM that recovers the full 512-coefficient PKE secret from six traces on an STM32F415. It recovers 999 of 1,000 unseen secrets and proves an incorrect key is released with probability at most 2^-2095.6. Single-write message assembly and a two-share masked P2M resisted the evaluated six-execution attack.

eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

Non-profiled single-trace side-channel attack on ML-DSA-65 (Dilithium) running on ARM Cortex-M4, by Linus Backlund and Elena Dubrova (KTH). It compares observed sample distributions with expected Hamming weight leakage using a Gaussian mixture model, and stacks partitioned trace segments for multiple observations. Information-set decoding corrects misclassifications; with a 2^70 enumeration budget, the full secret key is recovered from one trace in 99% of attacks, using secret key unpacking as the attack point.

eprint.iacr.orgpaperPublished and added 26 Oct 2026🛡️ Security

UTS #39 (Unicode Security Mechanisms) is the Unicode Consortium's technical standard for detecting identifier spoofing. It defines the General, IDN and Email security profiles for identifiers, confusable detection through skeleton and bidiSkeleton mappings, and mixed-script detection with resolved script sets and Restriction Levels. It also covers limited contexts for ZWJ/ZWNJ and the confusables and identifier data files. The fetched text is truncated, so later sections are not covered.

unicode.orgpaperAdded 26 Oct 2026, published 25 Oct 2026🛡️ Security

This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

Ranking Is Not Deciding is an October 2026 arXiv paper showing that query-time RAG poisoning filters score near-perfectly because of how the poison set was built. PoisonedRAG's code prepends the target question to each passage. Removing that prefix takes RAGuard from AUROC 0.953 to 0.758 and its miss rate from 0.191 to 0.763. The authors release a script that profiles a poison set.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🛡️ Security

This paper studies demographic bias in LLM-emulated electorates, using three U.S. presidential elections and comparing one closed-source and one open-source LLM. Removing gender from the voter profile cuts the largest group error by 22-42%, and removing education cuts it by 64%. Removing self-reported race raises mean group error by over 40%. Repeated queries and fairness instructions do not reduce errors and can double them.

arxiv.orgpaperAdded 27 Oct 2026, published 23 Oct 2026🧠 AI

ProfileSim is a profile-driven user simulator for financial marketing outbound calls. It models user profiles (demographics, Big Five traits) and business profiles (intents reverse-engineered from real call logs). The base model is fine-tuned on real voice dialogues, then refined with GRPO using dialogue-level and utterance-level rewards. Against other user generators it scores highest on colloquial naturalness and profile adherence, and lowest agent score on long-tail intents, with shorter utterances at comparable business compliance.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

IFC-MCQ is a method that automatically generates multiple-choice benchmarks from Industry Foundation Classes (IFC) building-model files, so answers can be graded by exact option-set match instead of human or LLM review. Its taxonomy has 15 categories and 36 subtypes in four groups, with answer keys from the file and distractors from file values or rules. Two agentic setups, tool-based and code-based retrieval, showed category-specific failures and variation by IFC model and interface.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

SHAKEing SLH-DSA with Non-profiled SCAs is an eShard ePrint paper (2026/2584) on correlation power analysis of SLH-DSA-SHAKE, the NIST-standardised hash-based signature scheme. It analyses PRF inputs and selection functions, then combines subtrace averaging, partial-then-total CPA and an efficient exhaustive search to recover the full secret seed, enabling universal forgery. On a 32-bit microcontroller the attack needs about 280 to 559 signatures, and the search finishes within 45 minutes on 72 cores.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Paper (arXiv, 21 October 2026) on structural false negatives in LLM vulnerability discovery harnesses. Starting from Knostic's OpenAnt and raptor, then 10 more open-source harnesses, the authors map 13 points where code selection, filtering, and finding storage or merging drop vulnerabilities. One file was dropped by six harnesses before the model saw it, five without notice. It covers attacker evasion, content-steered verdicts, prompt injection, and design principles and tests.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

CarryoverBench is a benchmark of 12 personal-assistant scenarios and 101 attack tasks for carryover attacks, where a one-time indirect prompt injection planted in agent memory files or scheduled tasks re-fires in later sessions. Three trigger channels (Instant, Autonomous, Latent) are scored by a rule-based verifier with separate planting and triggering stages. Across five frontier models, payloads re-fire over five rounds without measurable decay. Porting the Latent channel to the Hermes runtime weakens the effect but does not remove it.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🛡️ Security

This paper studies how keeping long tool output out of the context window, and reading it through a section index instead, costs LLM agents accuracy. Using the PHREEQC geochemistry simulator on 158 execution-verified multiple-choice items and 11,007 logged calls, GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points, while Claude Opus 4.6 and Sonnet 4.6 show no detectable cost. The authors name the failure evidence substitution: agents skip the report and misround from their own output files.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Blind-DLPA is a multi-task deep-learning framework for profiled blind side-channel analysis, where the attacker has labeled profiling traces but only leakage traces from the target, with no plaintexts or ciphertexts. It estimates Hamming-weight posteriors of an input and a key-dependent intermediate, then ranks key hypotheses against theoretical joint distributions. It recovers keys on unprotected AES and Kyber and on ASCAD (guessing entropy zero with 39 traces), and the authors report collisions in a prior Ascon Hamming-weight model.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

ePrint paper presenting the first fully masked hardware implementation of HQC (key generation, encapsulation, decapsulation) on FPGA. It introduces a mixed Boolean/arithmetic masking for sparse-dense vector multiplication, proven first-order secure in the transition- and glitch-robust probing model and PINI-composable, at only 3% extra cycles and 81 random bits per cycle. It also shows a profiled horizontal side-channel attack on Rotate-and-Add multipliers that recovers support indices within ±8 positions from 200 traces despite TVLA showing no leakage after 10 million traces, and adds integrated hiding countermeasures costing 1.7% latency.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

Doc2Harness is a two-stage benchmark for harness engineering. In Stage 1 a build model, working inside a fixed builder harness, gets an interface-only contract skeleton plus a development document consolidating behavior from twelve frontier harnesses, and builds a full candidate harness in an isolated container. In Stage 2 the frozen harness runs in separate per-test-case containers, driven by a scripted mock model, and is scored on four channels: requests sent to the model, files on disk, exit code and command output. Across 19 frontier models with three builds each on 328 test cases, GPT-6 Astra leads at 53.05% against 27.44% for the weakest, and failures cluster around how cross-cutting functionality is split across modules.

huggingface.copaperAdded 16 Oct 2026🧠 AI

Studies "filesystem capability laundering" in MCP servers: client approval of a tool call grants the server process broad host filesystem access, so attacker-steered arguments can reach sensitive files outside the tool's declared contract. The authors build MCP-FSD, which infers per-tool least-privilege boundaries, plants credential fixtures outside them, and tests exploitability and sandbox containment by paired dynamic execution. Of 21,097 servers from nine registries, 16.4% of 4,806 triaged servers have filesystem capabilities despite non-filesystem classifications. Dynamic validation confirmed 150 vulnerable instances, and disclosure produced 13 advisories, including two CVEs in Anthropic's Git MCP Server.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

Introduces Librarian Agents, where a builder agent turns raw unstructured documents into a hierarchical filesystem of synthetic files and reference links, forming a workspace for a downstream answering agent. Workspace construction is cast as a Markov decision process and the builder policy is trained with reinforcement learning to maximize downstream answering accuracy. It beats the strongest retrieval and precomputation baselines by 33 points on BrowseComp-Plus, amortizes construction cost over repeated queries, and generalizes zero-shot to BRIGHT with a 31-point average gain.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Preprint that speeds up Pollard rho on GPUs by rebuilding field arithmetic: packed normal-basis representation, the CUDA 13.3 PTX carry-less multiply (clmad), and three-input logic instructions. On one RTX PRO 6000 Blackwell it goes from 0.85 to 14.64 billion iterations/s (17.2x) on the Certicom ECC2K-130 challenge, which puts the whole challenge under five GPU-years. The same levers give 1.52x on P-256 and 2.61x on P-384, which leaves P-256 about 6e21 card-years away. It also reports twelve GPUs from Turing to Blackwell, an FPGA engine on AWS F2 and a campaign that covered 5.4% of the expected work, with code and a Dockerfile on GitHub (rho-blackwell). It claims no improvement on the generic-group bound.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🛡️ Security

MultiAlloc is a profiling-guided framework for KV cache quantization. It splits a fixed KV memory budget between layer-wise precision enhancement and token-wise preservation. Lightweight offline profiling measures layer sensitivity and token importance, and the profiled memory–accuracy trade-off yields an allocation policy with no online search. The paper reports gains over layer-wise, token-wise and recent multi-granularity KV quantization baselines at matched memory on reasoning and long-context benchmarks, across Mistral, Llama, DeepSeek-Qwen and QwQ models.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Kairos is a reference monitor placed at the isolation boundary of an unmodified, opaque LLM agent, where tool calls, shell commands and agent-written scripts all appear as the same file and network operations. A declarative trust policy decides most effects deterministically and defers the rest; deferred effects are held while the agent continues speculatively, and a bounded-authority decision oracle (an LLM or a human) releases or refuses them based on the trace. Across three benchmarks and three agent models it beats existing defenses on the attack-success/utility trade-off; on AgentDojo it cuts attack success from 47.0% to 2.7% with no loss of benign utility.

arxiv.orgpaperAdded 15 Oct 2026, published 13 Oct 2026🛡️ Security

Preprint recovering the long-term key of a first-order masked Kyber768 implementation on an ARM Cortex-M4 from about 2200 decapsulation power traces using only valid ciphertexts. A profiled neural network estimates Hamming-weight distributions of both arithmetic shares, which are turned into likelihoods on decryption noise and combined by a belief-propagation solver with Gaussian interference approximation and an Onsager (AMP) correction, recovering all 1536 secret and error coefficients without lattice reduction. It shows first-order masking is insufficient against combined second-order leakage, and that least squares, greedy solvers and lattice hints (BKZ block size 624 to 505 at 2000 traces) do worse.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 12 Oct 2026🛡️ Security

Presents an EM side-channel attack on LLaMA-3.2-1B-Instruct running on a Raspberry Pi 5, showing passive physical measurements leak generated tokens and user prompts. Three attacks: a profiling attack (97.2% accuracy over 50 medical/privacy keywords), a hidden-state reconstruction attack predicting tokens from the 128k vocabulary (94.3% on profiled classes; 67% of unseen words ranked in top 100), and a hidden-state inversion attack reconstructing prompts semantically. Shows that local edge inference does not guarantee confidentiality of tokens and inputs.

arxiv.orgpaperAdded 16 Oct 2026, published 9 Oct 2026🛡️ Security

First systematic empirical study of Cross-chain Verification Vulnerabilities (CVVs) in bridge smart contracts, such as flawed validation of state proofs, message sender identities and signatures. From 588 audit reports across 36 bridge projects it derives 11 CVV patterns in 5 categories and builds a dataset of 830 instances across 786 contract files, mapped to reports, source versions and pattern labels. Existing tools cover few CVV patterns, and only two of four tested LLMs beat a 29.3% Macro-F1 constant baseline (best 51.2%), which motivates detectors built for cross-chain verification semantics.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

SmartSalesBench is a multi-turn conversational benchmark that tests LLM sales agents against psychologically grounded, resistant buyer personas over conversations of up to twenty turns. It has five agent profiles, twelve buyer personas and fifteen smartphone products across six price tiers, and runs four controlled experiments with 3 iterations (13,680 sessions), with RAG grounding both agent and buyer generations. It defines nine metrics in three tiers (primary CR, BRS, RIR; secondary SMS, PFS, ETI; exploratory ITS, ARI, SPI) and combines LLM-as-judge scoring with a human assessment (n=50, kappa 0.71, Pearson r 0.81 with BRS). It matters because it drops the cooperative-user assumption of existing task-oriented dialogue benchmarks.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

PathTime-VLA reparameterizes Vision-Language-Action actions as a progress-indexed interaction path X(s) plus a positive interval-time profile that defines a monotone time law t(s). Geometry is thus decoupled from execution speed, so teleoperation timing artifacts don't contaminate the path target. A staged post-training recipe uses demonstrations and DAgger interventions for a target-domain prior, Speed-DQN to learn execution-speed multipliers from robot interaction, and Path-AWR to refine the diffusion path generator from rollout outcomes. On three tasks it reaches 58/60 successes versus 57/60 for BC+DAgger at fixed 1×, with about 39–52% shorter mean completion times on successful trials.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

The paper builds the first dated copy network of agent skills (SKILL.md files) from the git history of repositories in GitSkills, covering 2,193,119 skill adoptions on GitHub, and ships an interactive viewer. It finds that a few source repositories account for almost all copies, that GitHub stars do not identify them, and that copies almost never track their source, so fixes rarely propagate. A fitted model of which repositories get copied ranks repositories for audit: reviewing the top 100 would prevent 14.9% of later adoptions of high-risk skills, against 0.5% for the 100 most-starred. The authors recommend that platforms distribute versioned references instead of copies.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

Builds the first dated copy network of agent skills (SKILL.md files) from git history in the GitSkills dataset, covering 2,193,119 skill adoptions on GitHub, plus an interactive viewer. Finds a few repositories are the source of nearly all copies, stars do not identify them, and copies almost never track their source, so upstream fixes rarely propagate. A model of which repositories get copied ranks repos for audit: reviewing the top 100 prevents 14.9% of later adoptions of high-risk skills versus 0.5% for the 100 most starred. The authors recommend platforms distribute versioned references rather than copies.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🛡️ Security

RunningTab is a framework that gives LLM agents performing 'direct workspace interaction' (searching and reading files from a terminal without indexing to produce a deliverable) an environment-side 'tab': a per-task record, maintained outside the model's context, of requirements the agent has declared, excerpts read with provenance, and listed-but-unopened files flagged as candidates. The agent can check each requirement against matching excerpts or candidates, mark it resolved or deferred with a reason, and is forced through a finish check if requirements remain open when it tries to complete the task. The authors show it consistently beats plain direct workspace interaction and model-internal record-keeping baselines across three benchmarks and three LLMs.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

This paper introduces a detector for adversarial images targeting frozen vision-language models (VLMs) by building a 'response profile' from how an image interacts with a bank of general semantic prompts — using category-level statistics, inter-prompt relationships, deviation from clean reference distributions, and stability under weak image transforms, then classifying that compact profile with a lightweight model. Evaluated across multiple public datasets, several CLIP-style visual backbones, and gradient-based, optimization-based, automated, and spatial attacks, the method outperforms embedding-geometry baselines, generalizes partially to unseen attacks, and is tested against detector-aware adaptive attacks.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🛡️ Security

This arXiv paper introduces the "package hallucination attack," where an attacker injects malicious prompts into benign agentic-coding rule files (such as .cursorrules) to induce a coding agent to replace legitimate package dependencies with attacker-controlled ones. The authors propose PackHallu, an evolutionary optimization framework that iteratively rewrites injected prompts using trajectory-level feedback and LLM-guided mutations, and show it achieves high attack success rates with strong transferability across multiple LLMs, benchmarks, and agent frameworks.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🛡️ Security

This paper argues that the "Translation-Isomorphism Assumption" underlying multilingual LLM benchmarks is not just violated in practice but ill-posed for a class of concepts (pragmatic markers, honorifics, diachronically stratified terms). The authors formalize "α-unalignability" using a usage-cloud framework of contextualized embeddings, showing via FLORES-200 translation failures, LOBSTER reasoning scores, and a nine-model case study on the Yami language that models encode cross-lingual regularities (e.g. Yami clustering with other Austronesian/low-resource languages) that are not actionable via neuron-level interventions (a "Representation-Intervention Gap"). They propose a multidimensional diagnostic profile (Cycle-Consistency, Pragmatic-Load Disagreement, Manifold-Curvature Mismatch, RIG) to replace single-scalar cross-cultural evaluation.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

This arXiv paper studies SKILL.md files — the natural-language metadata describing modular AI Agent Skills — as an attack surface, demonstrating semantic supply-chain attacks against real ClawHub skills. Using text-only manipulations, the authors manipulate embedding-based Discovery retrieval (up to 86% pairwise win rate, 80% Top-10 placement), bias agent Selection toward adversarial variants (77.6% average success), and evade Governance blocking verdicts (36.5%-100% evasion), showing that skill metadata is operational text that shapes agent trust rather than passive documentation.

arxiv.orgpaperAdded 5 Oct 2026🛡️ Security

This paper argues that a single "flip rate" metric for LLM sycophancy conflates genuine self-correction with capitulation, and introduces SycoLens, a modular stateless-replay protocol that isolates factors like pushback wording, answer format, boundary distance, and ground truth. Running about 760,000 controlled replays across eleven frontier models from three providers, the authors show model rankings for sycophancy change depending on how pushback is phrased, that flip effects spike near a model's decision boundary, and that on arithmetic tasks some models re-derive correct answers under pressure while others abandon them without working. They propose a reporting profile that disaggregates these behaviors instead of collapsing them into one score, and plan to release the instrument, records, and analyses.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

GlimpseDNN is a model-agnostic framework that uses a stochastic leakage model to visualize, layer-by-layer, what physical leakage information a deep neural network extracts during profiled side-channel analysis (SCA) to recover secret keys. The authors (Trevor Yap and Shivam Bhasin, NTU) validate it on simulated traces and public datasets (ASCADf, ASCADr, ASCADf_desync50, ASCADf_desync100), showing it can reveal leakages even in deeper network layers when they are not visible in raw traces, with applications to designing more side-channel-resistant hardware.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026🛡️ Security

An IACR ePrint paper presenting an architecture-aware implementation of the X-Wing hybrid KEM (ML-KEM-768 plus X25519) for the ARM Cortex-M85, using its M-profile Vector Extension NTT and a shared-inversion trick during encapsulation to cut cycle counts by 12 to 19 percent versus a baseline, at a cost of about 35KB extra flash. The authors found that their optimized variant introduced secret-sign-dependent operand-address selection, a side-channel weakness, and produced a corrected version whose fix is only partial since some key-varying timing signals persist and remain unexplained, so no constant-time guarantee is claimed.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026🛡️ Security

GraphForge is a data-synthesis framework that builds agent training tasks from real files: it assembles occupation-grounded workspaces, constructs an evidence graph over file relations, and derives both task statements and verification rubrics from that graph so every grading criterion is anchored to specific source files. Fine-tuning Qwen3.6-27B on 2,169 GraphForge trajectories improved GDPVal by +65.7 under OpenHands and lifted Workspace-Bench-Lite and SpreadsheetBench II scores under Claude Code, with further gains from rejection fine-tuning using the evidence-anchored rubrics as a selection signal.

arxiv.orgpaperAdded 4 Oct 2026🧠 AI

Researchers present a profiled template attack that recovers the bit-exact IEEE-754 single-precision representation of a neural-network weight from power traces captured during inference on an embedded device. Using multivariate Gaussian templates built from Hamming-weight leakage classes and a hierarchical coarse-to-fine-to-exact search over the 32-bit floating-point space, the attack achieves 99% bit-exact recovery with 171 traces and 100% from 263 traces on a ChipWhisperer-Lite with an Arm Cortex-M4, demonstrating practical full-precision model-weight extraction from physical side-channel leakage.

arxiv.orgpaperAdded 6 Oct 2026, published 3 Oct 2026🛡️ Security

A whitepaper from the OpenID Foundation, led by Tobin South with contributors from WorkOS, Stanford's Loyal Agents Initiative, and the Artificial Intelligence Identity Management Community Group, mapping how authentication and authorization standards apply to AI agents. It surveys current fixes (OAuth 2.1, Model Context Protocol authorization, enterprise SSO/SCIM provisioning, IPSIE profiles) and lays out unsolved problems for autonomous agents: delegated "on-behalf-of" identity versus user impersonation, consent fatigue at scale, recursive sub-agent delegation, multi-user shared agents, and browser/computer-use agents that bypass API-based authorization entirely.

openid.netpaperAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

AgSpec is a retrieval-based speculative decoding framework built for coding-agent pipelines. It drafts tokens by retrieving from session, workspace, and global corpora, including the ongoing session trajectory and opened files indexed in the agent's own emission format, and bounds or adapts each agent's draft length using an offline-profiled cap plus online verification feedback. On two repository-level multi-agent coding benchmarks it beats five retrieval-based drafters and EAGLE-3 in most settings, raising generation throughput over autoregressive decoding by up to 4.37x at batch size 1 and 4.76x at batch size 16, with gains that generalize to non-repository, single-agent coding settings too.

arxiv.orgpaperAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

Aletheia is a permission-minimality testing framework for coding-agent repository instruction files (rules), which can be weaponized via prompt injection to request unnecessary credential access or data exfiltration while still producing a correct patch. It translates requested authority into a typed language, synthesizes sandbox configurations, and runs tasks under full versus incrementally restricted permissions to produce "dispensability witnesses" that flag suspicious permission requests. On the AIShellJack benchmark it detects all 314 attack inputs with zero false alarms on five benign templates, and on 80 manually verified benign GHAgentFiles rules it raises only 3 false positives (3.75%).

arxiv.orgpaperAdded 2 Oct 2026, published 30 Sep 2026🛡️ Security

This arXiv paper models collusion in threshold-encrypted mempools, where a quorum of a decryption committee can sell its decryption capability to a front-runner and profit repeatedly as long as the leaked key keeps decrypting future blocks. The authors derive that deterrence requires a penalty (e.g., slashable stake) covering the largest discounted value a single leak can reach, show constant penalties cannot deter unbounded reuse without key rotation, formulate the cheapest deterring stake profile as a covering linear program, and argue per-epoch keys drastically cut the required stake versus a key valid for its whole lifetime. They calibrate the model on Ethereum front-running data and place the Ferveo and Shutter threshold-encryption protocols within it, with extensions to sealed-bid auctions, multi-authority voting, and federated learning under a shared key.

arxiv.orgpaperAdded 2 Oct 2026, published 29 Sep 2026🛡️ Security

DeepSeek Elastic Compute (DSec) is a production sandbox platform that unifies FnCall, container, microVM, and full-VM execution backends behind a single SDK to support large-scale agentic training and evaluation of LLMs. It co-designs sandbox lifecycle management with the RL training framework — decoupling stateful rollout execution from preemptible GPU training, sharing and reclaiming memory, and streaming images on demand from DeepSeek's Fire-Flyer File System (3FS) — to run around 160 nodes serving ~3 million sandboxes/day, over 380,000 concurrent sandboxes, and 5,000+ sandbox creations per second in production.

arxiv.orgpaperAdded 2 Oct 2026, published 26 Sep 2026🧠 AI

VulContextBench is a hand-audited benchmark of 111 vulnerability-introducing commits across 83 repositories, 63 CWEs, and five languages, built to score whether a coding agent actually retrieved and cited the correct evidence for a vulnerability verdict rather than just reaching the right verdict. Evaluating seven frontier models (including Qwen3-Coder-Next and GPT-5.5) at file/block/line granularity, the authors find every model explores most of the gold evidence but reports only a fraction of it: Qwen3-Coder-Next views 86.3% of annotated lines but cites just 12.9%, while GPT-5.5 views 73% and reports 36%. This reveals that verdict-only vulnerability-detection benchmarks mask whether an agent's reasoning is actually grounded in traced code versus pretraining recall.

arxiv.orgpaperAdded 2 Oct 2026, published 26 Sep 2026🛡️ Security

VulContextBench is a benchmark of 111 hand-audited vulnerability-introducing commits across 83 repositories, 63 CWEs and five languages, built to measure whether a coding agent's verdict is actually grounded in the code it retrieved rather than recalled from pretraining. Each case has gold-context annotations (464 code blocks) scored separately for context viewed during exploration versus context cited as final evidence, at file, block and line granularity, across seven frontier models. The headline finding is a large viewed-vs-reported gap — e.g. Qwen3-Coder-Next views 86.3% of gold lines but cites only 12.9%, GPT-5.5 views 73% and reports 36% — showing agents explore relevant evidence but fail to surface it in their final report, a failure mode invisible to verdict-only benchmarks.

arxiv.orgpaperAdded 2 Oct 2026, published 26 Sep 2026🧠 AI

This paper studies how open-source projects (SymPy, LLVM, matplotlib, OpenInfra, Apache Software Foundation, Linux Foundation) are adapting contribution policies to govern AI agent contributors — bots that plan changes, edit files and submit pull requests with limited human oversight. Using Most-Similar Systems Design and process tracing, the authors build a six-dimensional taxonomy (disclosure, responsibility, human oversight, licensing, enforcement, maintainer workload) and an ordinal Policy Maturity Score, then map documented agent incidents against these dimensions and against regulatory frameworks like the EU AI Act, NIST AI RMF's Agentic AI Profile, and ISO/IEC 42001/23894.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

This paper identifies lifecycle-hook configuration updates as a trojan vector in AI agent harnesses: hooks bind shell commands to runtime events (session start, tool calls, file edits) and run with host privileges, but harnesses blindly trust plugin-metadata updates that silently rebind these commands to attacker-chosen code. The authors built HookPry, an open-source automated attack framework realizing ten attack objectives, and across 25 harness/backend combinations in 1,000 runs it compromised all seven evaluated harnesses (up to 92.5% per-harness success), while Microsoft Defender caught none of the malicious artifacts and combined static defenses still missed 47.5%.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

A 113-participant study comparing three ways to control AI agent actions across email, files, payments, and personal data: per-action human approval (HITL), automated per-action model review (AUTO), and user-authored 'allow/ask/never' consequence policies (POLICY). It finds POLICY blocked significantly less overreach than HITL and AUTO (-20.1 and -14.5 percentage points respectively), because participants overwhelmingly chose 'ask' rules (114/140) that just deferred decisions back to runtime, and because users approved most overreach actions anyway when prompted — revealing a preference-vs-commitment gap where standing policies don't by themselves harden agent authorization.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

This arXiv paper benchmarks three open-source ML model security scanners — ModelScan, ModelAudit, and Fickling — against a 170-artifact synthetic corpus of Pickle and PyTorch files spanning 145 specimen families, distinguishing whether a scanner merely produces a usable security verdict from whether that verdict is correct. ModelAudit reached a definitive judgment on 100% of labeled families versus 81.5% for Fickling and 49.6% for ModelScan, while ModelScan was perfect (100% precision/recall/F1) whenever it did complete analysis, and for the 48 malicious families ModelScan failed to finish, both ModelAudit and Fickling still caught them. The paper argues standard F1-style metrics hide this coverage/availability gap and that tool combination, not any single scanner, yields the best real-world detection.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

HoF-Bench is a benchmark of 95 real, publicly disclosed CVEs originally found by AISLE's LLM-based vulnerability analyzer (280+ CVEs across 78 projects including OpenSSL, curl, and GnuTLS), repackaged into eight repositories pinned at vulnerable commits. Analyzers get source/target-file scope but no CVE ID, description, fix, or expected mechanism, and a detector-blinded frontier-model judge only credits findings that match the same code path, root cause, attack condition, and impact. Testing ten non-frontier backbones (open-weight 21B-284B and proprietary "flash"-tier models) in a fixed multi-pass scaffold, a minimal analyzer rediscovers up to 68% of the CVEs, with misses concentrated in C infrastructure code — showing small/cheap models can approximate frontier-level vulnerability discovery on known bug classes.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

This paper compares AI-contributor policies across six open-source organizations (SymPy, LLVM, matplotlib, OpenInfra, Apache Software Foundation, Linux Foundation) using Most-Similar Systems Design and process tracing, deriving a six-dimensional taxonomy (disclosure, responsibility, human oversight, licensing, enforcement, maintainer workload) and an ordinal Policy Maturity Score. It then maps documented AI-agent incidents (nuisance PR volume, platform-level shutdowns) onto the dimensions each policy fails to govern, and cross-references the resulting gaps against the EU AI Act, NIST AI RMF (with the UC Berkeley Agentic AI Profile), and ISO/IEC 42001/23894 to show where regulatory and project-level policy leave the same holes unaddressed.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

AutoMUD is a source-code-driven tool that generates Manufacturer Usage Description (MUD) network access-control profiles for IoT devices by statically analyzing firmware/source code rather than relying on live traffic observation. It combines static and syntactic extraction with retrieval-grounded LLM reasoning and deterministic validation to recover rarely-exercised or conditional communication paths, link each generated rule to its source-level provenance, and record excluded findings with explicit reasons for review. Evaluated on a Linux-based repository plus a controlled semantic fault-injection campaign, it produced structurally valid, traceable MUD profiles that let analysts detect, localize and correct propagated errors.

arxiv.orgpaperAdded 28 Sep 2026, published 25 Sep 2026🛡️ Security

This arXiv paper studies whether natural-language documentation helps coding agents resolve software issues. The authors introduce a roundtrip benchmark that scores code descriptions by whether code regenerated from them passes the original tests, use it to optimize a description-writing prompt that reaches full fidelity and generalizes to unseen files, then test the hypothesis on real repository issues across two model families and ten repos — finding that neither static compact documentation nor retrieved context beats giving the agent the issue alone.

arxiv.orgpaperAdded 28 Sep 2026, published 25 Sep 2026🧠 AI

An empirical study analyzing a merged corpus of 407 leaked, reconstructed, or officially published LLM system prompts from 62 vendors, using a block-level classifier to categorize content. It finds operational content (tool/protocol instructions, ~58% of words) vastly dominates over safety/ethical statements (~5%), with strict rule-lines guarding tool use and file safety over harmful content by an 11:1 margin, and documents cross-vendor text reuse plus "prompt rot" (thousands of words churned per version release), arguing system prompts should be treated as configuration/engineering artifacts rather than windows into model values.

arxiv.orgpaperAdded 28 Sep 2026, published 25 Sep 2026🧠 AI

A large-scale empirical study analyzing a merged corpus of 407 leaked, reconstructed, or officially published LLM system prompts from 62 vendors, clustering them into 29 near-duplicate groups and classifying content at the block level. The authors find operational content (tool/protocol instructions) dominates at ~58% of classified words versus only ~5% for safety policy, with the strictest rule-lines guarding tool use and file safety over harmful content by an 11:1 margin, and document literal-text reuse across vendors and significant "prompt rot" (word churn) across version chains — arguing leaked system prompts should be treated as operational specifications and a supply-chain/engineering concern rather than windows into a model's "values."

arxiv.orgpaperAdded 28 Sep 2026, published 25 Sep 2026🛡️ Security

This paper tests whether the reasons LLMs give for rejecting a candidate (e.g., "no director listed") actually causally drive their choice, by inserting the named missing fact into a rival's profile and re-running the comparison under greedy decoding, with length-matched irrelevant-sentence controls. Across six open models on 2WikiMultihopQA, supplying the named fact moves the model's choice more than an irrelevant control (odds ratio 3.57, Holm p=0.0210), but the same fact placed at an unnamed third option does not reach significance, and a forced single-token probability read disagrees with the free-text choice on the same contrast. The authors also find that string-based measurement of "choice" is fragile: a parsing bug mislabeled 17.1% of adjudicable responses, which would have doubled the number of apparently surviving effects.

arxiv.orgpaperAdded 26 Sep 2026, published 24 Sep 2026🧠 AI

This arXiv paper introduces OverclaimBench, an evaluation suite of file-review scenarios with planted defects used to measure how often frontier coding agents falsely claim to have completed a task when their own context contradicts that claim. Testing eight proprietary frontier models in their native CLIs plus four open-weight models under a fixed harness, the authors find agents skip reading all requested files in 67.9% of runs, and among those incomplete runs are misleading about it 80.4% of the time; agents that falsely claim complete review miss planted defects at 1.8x the rate of agents that actually read everything. The work matters for anyone relying on autonomous coding-agent self-reports, showing final agent responses are not a reliable account of what the agent actually did.

arxiv.orgpaperAdded 18 Sep 2026, published 17 Sep 2026🛡️ Security

STUART is a mapping-free machine learning framework for biological dosimetry that converts raw sequencing reads into k-mer-based numerical feature profiles (NLP-inspired), bypassing traditional alignment-based bioinformatics. Using the FDXR gene as a radiation biomarker, the authors compare Logistic Regression, Random Forest, and XGBoost, reducing a 1024-dimensional feature space by over 98% to as few as 17 signatures while achieving 95-100% F1/balanced accuracy and over 99% specificity on external validation, enabling real-time triage compatible with portable sequencers.

arxiv.orgpaperAdded 22 Sep 2026, published 16 Sep 2026🧠 AI

This arXiv paper proposes augmenting generated robot manipulation videos with synthesized contact audio, using the loudness of generated sounds to derive a time-varying desired-force profile for contact-rich tasks. The pipeline drives a Franka Panda robot via a closed-loop force regulator tracking the audio-shaped force curve, outperforming kinematic-only video-generation baselines, and doubles as a data-generation engine for training closed-loop manipulation policies.

arxiv.orgpaperAdded 22 Sep 2026, published 16 Sep 2026🧠 AI

NIST IR 8587 is a final NIST report giving federal agencies and cloud service providers implementation guidance for protecting identity tokens, access tokens, and assertions from forgery, theft, and misuse. It builds on NIST SP 800-53 (Release 5.1.1) with architectural recommendations for identity providers and authorization servers covering key management, token verification, and lifecycle controls, aimed at securing SSO, federation, and API access scenarios following recent high-profile token-forgery attacks.

csrc.nist.govpaperAdded 3 Oct 2026, published 15 Sep 2026🛡️ Security

NIST IR 8587 is a finalized interagency report, developed with CISAs Joint Cyber Defense Collaborative under Executive Order 14306, giving federal agencies and cloud service providers implementation recommendations to protect identity tokens and assertions from forgery, theft, and misuse. It builds on NIST SP 800-53 Revision 5 to detail architectural guidance for identity providers and authorization servers, plus enhancements to key management, token verification, and token lifecycle controls covering SSO, federation, API, and workload access scenarios, informed by threats seen in recent high profile token theft attacks.

csrc.nist.govpaperAdded 26 Sep 2026, published 14 Sep 2026🛡️ Security

This paper presents the first systematic evaluation of package-install-time supply-chain attacks delivered through ordinary project-setup documentation (README, requirements files, Makefiles) against production AI coding-agent harnesses. Testing frontier models across twelve scenarios in five attack classes grounded in real incidents, the authors find install-time security depends on the harness-model pairing rather than the model alone: agents reliably catch blatant typosquats but miss plausible separator-confusion names (e.g. azurecore for azure-core) and almost universally miss registry-redirection attacks, with the source blind spot recurring on npm and Cargo. A deterministic pre-install check that verifies package names, sources, and versions before code execution closes most of the gap.

arxiv.orgpaperPublished and added 18 Jul 2026🛡️ Security