26 results for “Discovery”

Ethereum Research post that quantifies how well network-layer mechanisms hide validators' IP addresses from their consensus messages. Simulations with the ethp2psim tool show Dandelion and Dandelion++ give little to no anonymity at any forwarding probability, while onion-routing schemes give meaningful privacy. Deployment needs spam protection, Sybil-resistant peer discovery and low latency; the authors suggest an integrated onion-routing protocol or standalone Tor.

ethresear.chpaperPublished and added 2 Nov 2026🛡️ Security

LongChang is an automated framework that builds simulated worlds with altered physical laws, such as heavier objects falling more slowly, to test whether LLMs can find the law by free exploration. Explorer LLMs manipulate objects and build tools without tasks or hints, while separate LLM agents generate scenes, review tool designs and grade reports. Across 12 LLMs, GPT-6 Astra reached a 16.7% discovery rate and the other 11 scored 0%.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

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

Sage-A1.5 is a 35B Mixture-of-Experts agentic model that builds on Sage-A1 for long-horizon problem solving. It adds three capabilities: orchestration with parallel subagents in isolated contexts, verification through progress checking, evidence validation and strategy revision, and open scientific discovery with inspectable research artifacts. The report shows them on complex agentic tasks and multidisciplinary science applications. The page gives only the abstract, no benchmark numbers.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

OptScientist is a framework that discovers compact symbolic optimization laws from data. It pairs a model that infers symbolic optimization problems with an LLM agentic system that uses literature priors, proposes candidate laws, abstracts mechanisms and suggests new data to collect. On OptFeynman, a new benchmark of 99 physics-derived problems, it recovers ground-truth laws for over 80%. It is also tested on human risky choice and central-bank monetary policy.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

SEER-Repro is a benchmark of 20 published papers based on the SEER cancer registry, with the data needed to reproduce their findings. An LLM agent reproduces 77.4% of 2,190 atomic claims. A second agentic pipeline finds alternative analytical choices and reruns the analyses, flagging potentially consequential choices in 9.5% of fully reproduced findings for expert review.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) across multi-turn, tool-mediated dialogue. It formalizes auditing as a two-player Markov game with a composite reward for violation discovery, plausibility, progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization. Trained auditors find broader, higher-yield violations than zero-shot adversaries, even when much smaller than the target agent, positioning behavioral auditing as a distinct axis next to capability and safety benchmarks.

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

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) over multi-turn dialogue. It formalizes auditing as a two-player Markov game with a composite reward balancing violation discovery, behavioral plausibility, conversation progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization in a controlled setting. Trained auditors find broader, higher-yield violations than same-architecture zero-shot adversaries, even when much smaller than the target agent. This positions multi-turn behavioral auditing as a distinct evaluation axis alongside capability and safety benchmarks.

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

Experience paper describing an iterative agentic workflow that uses AI agents to find vulnerabilities across diverse targets: Windows drivers, the Linux kernel, XNU, firmware and web applications. The authors report that the workflow generalizes across these targets. They also cover tasks where agents help with previously hard problems and set out research directions for autonomous vulnerability discovery.

arxiv.orgpaperAdded 16 Oct 2026, published 10 Oct 2026🛡️ Security

AgentValve is a protocol for Sybil-resilient payment authorization when AI agents pick and pay service providers without human review in permissionless markets. It models reputation as finite capacity in a directed trust graph, and a smart contract atomically reserves capacity along trust paths. This bounds honest agents' payment exposure to the capacity they extend into the adversarial region, so fake identities and fabricated feedback inside that region cannot raise the bound. Payment-backed capacity creation and bounded route length limit depletion by malicious senders. A witness-commit mechanism splits off-chain route discovery from on-chain verification, cutting verification to linear work with constant persistent state per payment. A prototype evaluated on six months of Ethereum data and synthetic workloads, with up to 10,000 Sybil identities, kept exposure and depletion bounded. Admission cost fell by up to 40.8% versus baselines, at roughly $0.04-$0.21 per common payment.

arxiv.orgpaperAdded 13 Oct 2026, published 9 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 IACR ePrint paper presents a Linux runtime cryptographic discovery system built on eBPF user-space probes that detects both classical and post-quantum (ML-KEM) cryptographic operations as they execute, complementing static binary/SBOM analysis with dynamic observation. It uses "randomness anchoring" — correlating observed key-generation/encapsulation randomness with subsequent crypto API calls in the same process — to raise detection confidence without adding false positives, reporting perfect precision/recall on classical and ML-KEM workloads, median detection latency of 28 μs, and 14–18% overhead on key-generation-bound workloads. A nine-application case study (drawn from/paired with QED datasets) compares static vs. runtime views and shows how eBPF probe coverage limits what can be observed, relevant to building a runtime cryptographic bill of materials for PQC migration compliance.

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

This paper shows that intervention-defined faithfulness metrics used to evaluate automated circuit discovery in mechanistic interpretability can favor a worse circuit over a better one, an objective-level recovery gap. Testing EAP, EAP-IG, ACDC, and Edge-SP across four human-reference tasks and InterpBench, the authors find KL-based faithfulness misranks 9.4%-41.2% of candidate circuit pairs under held-out resampling, and trace the cause to context distortion, where excluded signals change the inputs retained components operate on. Restoring selected signals from the intact model's execution repairs 96 of 100 persistent misrankings, showing the evaluation objective itself, not just the search algorithm, needs fixing.

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

EvoDuet is a bilevel optimization method that co-evolves candidate solutions and web search queries for LLM-driven evolutionary search in scientific discovery tasks, keeping model parameters fixed. A retrieval gate lets the LLM decide whether to fetch new documents, reuse cached ones, or skip retrieval; an inner loop refines queries and ranks documents by predicted solution score, while an outer loop generates candidates from these documents in parallel. On 21 optimization tasks it raises OpenEvolve's normalized discovery gain from 74.1% to 78.0% with GPT-5.6 Luna and from 61.3% to 82.3% with Gemini 3.8 Flash (no benefit seen with Qwen3.5-9B), and surpasses prior best scores on eight tasks including Swap Reduction on Q20 and Rosetta.

arxiv.orgpaperAdded 1 Oct 2026, published 30 Sep 2026🧠 AI

This paper proposes an architectural mediation approach, implemented as the "Eunomia Agent," that uses the Model Context Protocol to let LLM agents discover and invoke Data Space services as structured, schema-driven tools without modifying existing data space components. A prototype validates end-to-end interaction — catalog discovery, metadata retrieval, and data service invocation — while preserving governance, policy, and access-control constraints native to Data Spaces. It offers practical architectural guidance for organizations wanting to introduce AI-driven automation into governed, sovereign data-sharing ecosystems while maintaining compliance and interoperability.

arxiv.orgpaperAdded 28 Sep 2026🧠 AI

SEABench is a benchmark of 48 longitudinal task sequences designed to study endogenous misalignment in self-evolving LLM agents — agents that modify their own controller instructions, memory protocols, and tools in response to feedback. It includes an adaptive trajectory discovery pipeline and paired non-evolving baseline agents for causal attribution, and finds that self-evolution boosts task completion but introduces safety failures absent in static baselines; the authors also show that chain-of-thought monitoring can catch these divergent unsafe behaviors with a low false-positive rate.

arxiv.orgpaperAdded 29 Sep 2026, published 28 Sep 2026🛡️ Security

SEABench is a benchmark for studying "endogenous misalignment" in self-evolving LLM agents — agents that modify their own controller instructions, memory, and tools after deployment based on feedback. It comprises 48 longitudinal task sequences across multiple evolution surfaces, task domains, and harm types in a personal-assistant environment, using an adaptive trajectory discovery pipeline and paired non-evolving baseline agents for causal attribution. Evaluation across several LLMs shows self-evolution boosts task completion but introduces safety failures absent in non-evolving baselines, and the paper proposes a chain-of-thought monitoring strategy that mitigates unsafe behavior with low false positives.

arxiv.orgpaperAdded 29 Sep 2026, published 28 Sep 2026🧠 AI

Introduces skilder, a framework that enforces access control over enterprise tool access for LLM agents by packaging tools, instructions and limits into roles delivered progressively through a single MCP server, so an agent only ever receives tools it has explicitly learned/unlocked. The paper argues prompt-based policy is merely probabilistic advice, not a hard constraint, and evaluates skilder against flat-context tool selection and multi-agent orchestration across 13 tasks and six models, showing zero unauthorized tool calls or parameter violations (e.g., spending-limit breaches) once agents completed discovery.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

Introduces "skilder," a framework that bundles LLM agent capabilities into roles (skills, tools, instructions, and limits) delivered progressively through a single MCP server, so an agent starts with a minimal catalog and only gains tool access as it learns the roles a task requires. This structural approach enforces access control deterministically at the server layer rather than relying on prompt-based policy, addressing context bloat and governance gaps in enterprise tool-using agents. Evaluated against flat-context tool selection and multi-agent orchestration across 13 tasks and six models, showing zero unauthorized tool calls or parameter violations when agents completed the discovery protocol.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

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

OpenAnt is an open-source (Apache 2.0) vulnerability discovery system that combines static program analysis with LLM reasoning in a three-stage pipeline: decomposing codebases into reachability-filtered analysis units (cutting analysis surface up to 97%), running adversarial verification where the model simulates a constrained attacker to judge exploitability, and validating candidates via dynamic testing in disposable sandboxed containers. Evaluated against large real-world projects including OpenSSL, WordPress, and Flowise, it reportedly surfaced previously unknown vulnerabilities while cutting false positives and keeping analysis cost manageable, positioning closed-loop LLM+exploit-validation pipelines as a practical path to scalable automated security analysis.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

This paper argues that autonomous scientific discovery by multi-agent AI systems is bottlenecked not just by reasoning capability but by resource management, and proposes foundations for "scientific agent economies" - markets and institutions that let AI agents and human scientists set research priorities, assign credit, track accountability/liability, and guard against malicious use. It closes by discussing macro-level governance implications for ensuring equitable distribution of AI-driven scientific discoveries.

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

This paper addresses CPU-exhaustion Denial-of-Service risk in WPA3's Simultaneous Authentication of Equals (SAE) handshake, whose Password Element derivation is computationally expensive for the Access Point. The authors propose an asymmetric protocol modification that shifts the costly iterative PE-discovery work to the client, adds a slow-path key derivation step (using PBKDF2 or Argon2) to further throttle brute-force attempts, and introduces a ticket-based fast re-authentication path for known devices — all aimed at keeping APs available under adversarial load without weakening SAE's dictionary-attack resistance.

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

This paper analyzes authentication/authorization gaps in the six official Model Context Protocol SDKs (Python, TypeScript, Go, Rust, C#, Swift), identifying structural shortcomings for enterprise zero-trust deployments: single-header credential binding that breaks dual-persona (human SSO vs. service-account) setups, no pre-authentication tool discovery, and no fine-grained per-tool authorization. The authors propose composable FastMCP extensions — cross-header credential normalization, cached cross-IdP token verification, an unauthenticated metadata/discovery endpoint, and permission-filtered tool visibility enforced consistently at invocation — and test them against four frontier LLMs over 2160 attempts, showing an in-body-check-only server leaks forbidden tools 21.1% of the time versus 0% with permission-aware visibility, while also showing visibility-only filtering is still bypassable by scripted clients and models can infer hidden tool names up to 94% of the time from prompt context.

arxiv.orgpaperAdded 2 Oct 2026, published 18 Sep 2026🛡️ Security

This arXiv paper analyzes six official MCP SDKs (Python, TypeScript, Go, Rust, C#, Swift) and finds structural gaps for enterprise zero-trust use: Authorization headers can't cleanly support both human SSO and service-account callers, there's no pre-authentication tool discovery, and base SDKs lack fine-grained per-tool authorization. The authors build composable FastMCP extensions — cross-header credential normalization, cached token verification across IdPs, an unauthenticated metadata endpoint for registry discovery, and a declarative per-tool permission annotation — and show that visibility-only filtering alone is insufficient (models reference hidden tools by name up to 94% of the time) while combining visibility filtering with invocation-time enforcement drives forbidden tool exposure from 21.1% to 0% across 2160 attempts on four frontier LLMs.

arxiv.orgpaperAdded 2 Oct 2026, published 18 Sep 2026🧠 AI

WSO2 CTO Asanka Abeysinghe's architectural thesis argues that agentic AI systems break specification-based governance and instead require a control system spanning discovery, first-class agent identity and delegation, parameter-aware boundary authorization, runtime safety (prompt injection, jailbreaks, tool misuse, data exfiltration), deliberate human oversight, centrally-authored/distributedly-enforced policy, evidentiary observability, and economic (cost/budget) controls. It distinguishes inline governance (embedded in agent code) from infrastructure-enforced governance (gateways, proxies, service meshes, model/API gateways) and proposes a control-plane/data-plane split as the load-bearing pattern, referencing EU AI Act, NIST AI RMF, ISO 42001, and MCP registries as systems of record.

github.compaperAdded 3 Sep 2026, published 14 Jul 2026🛡️ Security