CompSkill is a skill retrieval method for LLM agents that indexes skill documents with learned 512-d keys and compiles matched same-task execution comparisons into skill offsets and capped key residuals (KeyOpt). Experience changes which document the executor receives, not its text. Compact keys lift SkillRet NDCG@10 from 64.0 to 70.9, and it completes 256 of 332 ToolQA tasks with GPT-5.6 Luna.
BM25LLM
CompSkillKeyOptSkillRet+8
research, agents, llm-appsΒ· Added 3 Nov 2026, published 31 Oct 2026
SkillCDG is a compliance detection framework that turns each long SKILL document (an enterprise SOP followed by LLM agents) into a constraint dependency graph. Atomic rules are nodes tied to source text spans, and mandatory prerequisites are the only edges. Policy-generated keywords seed rules, and their dependency closure fills the small auditor's context budget. Retrieval is trained with GRPO and selective on-policy distillation. It beats full-document and retrieval baselines on three enterprise datasets and two public benchmarks.
GRPOLLMRAG
SkillCDGarXiv
research, llm-apps, trainingΒ· Added 3 Nov 2026, published 31 Oct 2026
AgentCap is a runtime authorization layer for LLM agents that moves the authorize decision out of the model and mediates every tool call through an MCP wrapper. It issues short-lived, budget-bounded capability tokens checked at the tool boundary, restricts delegation to attenuating parent-child chains, and uses a declarative policy language. On AgentDojo and InjecAgent it lowers attack success in every tested setting, reaching 0% with GPT-5.4.
Model Context ProtocolLLMAgentCap
indirect prompt injectionAgentDojoInjecAgent+1
AI SecurityΒ· Added 3 Nov 2026, published 31 Oct 2026
AgentCap is a runtime authorization layer for LLM agents from Virginia Tech (Suresh and Gao), posted to arXiv on 31 October 2026. The model proposes MCP tool calls, and a policy engine issues Ed25519-signed capability tokens only after checks on provenance, intent, trust and prerequisites. It reports 0% attack success with GPT-5.4 on AgentDojo and InjecAgent, but only on scenarios the authors built.
Model Context ProtocolEd25519JWT+1
Virginia TechAgentDojoInjecAgent+4
agents, mcp, researchΒ· Added 3 Nov 2026, published 31 Oct 2026
Rohan Paul's X post summarizes a TikTok paper on MCP attacks. LLM agents often finish the requested task, then quietly call an attacker-controlled logging tool that records the user query, the server reply and the final answer. The attack works because agents read natural-language tool descriptions when choosing and ordering tool calls. The visible text is truncated and gives no success rates or defenses.
Model Context ProtocolLLM
tool poisoningdata exfiltration
AI SecurityΒ· Added 30 Oct 2026, published 29 Oct 2026
CORAL (Cognitive Resource Self-Allocation) is an agent-callable working-memory toolset that lets an LLM agent checkpoint task progress and verified facts, then reset its cluttered context and resume from a clean state. A Multi-episode Agentic Reinforced Policy Optimization algorithm trains the checkpointing. With an 8B model it reaches 42.8% average accuracy on GAIA, uses 6-7x fewer input tokens than a ReAct agent, and adds 8.0 points to a proprietary agent on BrowseComp.
SEAL is a defense against indirect prompt injection for local LLM agents. It learns a soft prompt and scoring readout offline, then uses a frozen task model to score and delete injected tokens from tool outputs, with queries built from trusted initial context. On AgentDojo, attack success falls from 34.6% to 0.1% and Joint rises from 51.4% to 77.5%, using about 127x fewer defense tokens than PISanitizer.
LLMAgentDojoAgentDyn+1
indirect prompt injection
AI SecurityΒ· Added 31 Oct 2026, published 29 Oct 2026
REALM (Required Execution Assets Linked to a Mission) is an approach to applying least privilege to LLM agents by identifying the assets a task needs, including ones discovered at runtime. It models assets as Dependency Graph nodes, separates discovering an asset from authorizing it, and exposes resource-bound Partial APIs inside a Task Container. On AgentDyn with qwen3.5-flash, attack success rate falls from 16.6% to 0.2%, with 40.0% task completion on clean tasks and 36.4% on injected cases.
QwenLLMAgentDyn
prompt injectionleast privilegeREALM
AI SecurityΒ· Added 31 Oct 2026, published 29 Oct 2026
Rohan Paul's X post summarizes the paper "Log-To-Leak: Prompt Injection Attacks on Tool-Using LLM Agents via the Model Context Protocol", from TikTok researchers. It reports that MCP-using agents often finish the requested task, then quietly call an attacker-controlled logging tool that records the query, server reply and final answer. The attack works through natural-language tool descriptions that agents read when choosing and ordering tools. The visible text is truncated.
Model Context ProtocolLLM
Log-To-Leakprompt injectionTikTok+1
AI SecurityΒ· Added 29 Oct 2026, published 28 Oct 2026
Log-To-Leak is an arXiv paper (cs.CR, cs.AI) by Yuepeng Hu, Chongyu Fan, Sirat Samyoun and Jian Du on prompt injection attacks against tool-using LLM agents through the Model Context Protocol. The title points to a logging-based data leakage channel. The fetched page holds only the title, authors and arXiv link, with no abstract, so the method and results are not visible here.
Model Context ProtocolLLM
Log-To-Leakprompt injection
AI SecurityΒ· Added 29 Oct 2026, published 28 Oct 2026
JevAct is a runtime verification framework that intercepts selected LLM agent actions before execution and returns feedback for bounded revision. It compiles domain policies into executable state checks and structured questions scored by a prefill-only decision model, giving probability-based judgments without generation. Tested on eleven agents with tau^2-bench Airline and Telecom and BFCL, it gained consistently on Telecom and BFCL and was mixed on Airline.
LLM
JevActtau^2-benchBFCL+1
research, agents, evalsΒ· Added 30 Oct 2026, published 28 Oct 2026
Repairs for Whom? studies single-step repairs of failed LLM agent trajectories and asks whether common evaluators (greedy replay, stronger models, oracles, log-reading judges) pick repairs that help the deployed agent. Using independent rollouts in ALFWorld and WebShop, greedy replay loses 0.06-0.13 in expected gain, and Qwen2.5-14B rollouts misselect repairs for Qwen2.5-7B. The authors recommend verifying repairs with the executor that will run them.
LLMQwen
ALFWorldWebShopQwen2.5-7B+2
research, agents, evalsΒ· Added 30 Oct 2026, published 28 Oct 2026
AgentAnchor is a session-keyed behavioral watermark for attributing LLM agent trajectory logs to an owner. It derives per-step keys from a secret key, a fresh public session ID and semantic observation states, and embeds parity evidence through a KL-bounded exponential tilt. The paper formalizes learned cross-session forgery, and a trusted verifier checks disputed logs using wrong-key calibration and abstention.
LLM
AgentAnchorlearned forgery
AI SecurityΒ· Added 30 Oct 2026, published 28 Oct 2026
AgentAnchor is a session-keyed behavioral watermark that lets an agent owner prove ownership of a trajectory log. Each step key comes from a secret key, a fresh public session identifier and semantic states of recent observations. Parity evidence is embedded through a KL-bounded exponential tilt of action choices. A trusted verifier checks disputed logs without rerunning the agent, using wrong-key calibration and abstention. The paper also formulates a learned cross-session forgery threat.
LLM
AgentAnchorarXiv
research, agentsΒ· Added 30 Oct 2026, published 28 Oct 2026
This paper treats LLM reasoning effort as a credence good and measures the counterfactual effect of effort levels on proprietary and open-weight models across four benchmarks, including long-horizon agentic tasks. Higher effort often fails on tasks lower effort solved, and on easy tasks the top level costs up to 42x the lowest. Starting at the lowest effort and escalating only after a verified failure matches or beats fixed top effort, usually at lower cost for proprietary models.
LLM
Hugging Face
research, evals, agents, ai-policyΒ· Added 28 Oct 2026
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%.
LLM
LongChangGPT-6 AstraarXiv
evals, research, agentsΒ· Added 28 Oct 2026, published 27 Oct 2026
Log-To-Leak is a prompt-injection attack on tool-using LLM agents that abuses Model Context Protocol tool metadata to trigger an attacker-controlled logging call after the last legitimate call, leaking the user query, server name, server response and agent response. Evaluated on five MCP servers and four agents, it reaches 62.64-100% attack success with 76.55-95.64% logging accuracy. Three defenses leave over 55% residual ASR on GitHub MCP with GPT-4o.
Model Context ProtocolGitHubGPT-4o+1
Log-To-Leaktool poisoningprompt injection
AI SecurityΒ· Added 28 Oct 2026, published 26 Oct 2026
SentryBench is a benchmark for tool-using LLM agents in public-safety deployments. It scores attack success, over-refusal (missing a real emergency) and over-reaction (unwarranted action) on a cost-sensitive frontier, using real emergency-call transcripts, disaster messages and injection payloads. Across 840 episodes on three frontier models and three defenses, refuse-more caution pushed over-refusal to 100 percent on two models, while a trust-gating wrapper gave the lowest harm.
LLM
SentryBencharXiv
evals, ai-safety, agentsΒ· Added 27 Oct 2026, published 25 Oct 2026
SentryBench is a benchmark for tool-using LLM agents in public-safety settings (public alerts, responder dispatch, citizen-record queries). It scores attack success, over-refusal and over-reaction on a cost-sensitive frontier, using real emergency-call transcripts and real injection/jailbreak payloads. Across 840 episodes on three frontier models and three defenses, refuse-more caution raised over-refusal to 100 percent on two models, while a trust-gating wrapper gave the lowest harm on every model.
LLM
SentryBenchover-refusalover-reaction+1
AI SecurityΒ· Added 27 Oct 2026, published 25 Oct 2026
Continual Learning Exam (CLE) is a benchmark of 120 human-authored tasks, each a five-level ladder (600 levels) across 89 professional roles and 12 occupational domains, built by 50+ domain experts. Agents must pass a level to unlock the next, and an evaluation agent scores submissions against human rubrics. Across 17 harness-model configurations, feedback plus one revision adds a median 5.3 points; the best system clears 47.7% of levels and finishes all five levels in 23.3% of tasks.
LLM
Continual Learning ExamarXiv
evals, agents, researchΒ· Added 27 Oct 2026, published 25 Oct 2026
wtfibought (llm-trading-arena) is an MIT-licensed live arena where LLM agents trade virtual capital on real market data with all reasoning public. Users bring their own model and key, set the wake candle interval, leverage range and margin budget, with at most three symbols and twelve model calls per wake. The stack is Spring Boot on Java 25 with Spring AI, PostgreSQL, Redis and a React 19 frontend.
Spring AISpring BootJava+10
wtfibought
agents, evalsΒ· Added 26 Oct 2026, published 23 Oct 2026
This paper studies experience reuse for LLM agents that optimize ligands in protein pockets by editing and docking. Using the full log of one agent run over CrossDocked2020 test-set pockets, it tests whether keys in an experience base recur on unseen pockets and separate improving from worsening edits. The separation is largely explained by molecule size change, action and round, not the target. The authors conclude a base may better prioritize an agent's own proposals for docking than prescribe what to build.
LLM
CrossDocked2020arXiv
research, agentsΒ· Added 26 Oct 2026, published 23 Oct 2026
MCP-Injector is a protocol-aware fault-injection middleware for Model Context Protocol tool calls, with permanent, transient and intermittent faults on seedable schedules. Its fault model comes from 57,240 issue and PR threads across 1,774 open-source MCP repositories. In a 1,350-cell experiment, faults cut task-completion score by 60% (8.08 to 3.23 of 10), and the 120B gpt-oss model was no more resilient than the 20B. Agent stacks differed in how well they absorbed faults.
Model Context ProtocolOpenAI Agents SDKLangChain+2
MCP-Injectorgpt-oss-120bOpenAI
mcp, evals, agents, researchΒ· Added 26 Oct 2026, published 23 Oct 2026
AKV introduces cache engineering, which lets LLM agents control their active KV cache with drop (exclude cache from future attention) and repos (adjust retained keys' position encodings) without editing the trajectory or re-prefilling. The AKV serving engine keeps a stateless API while managing stateful cache reuse, eviction and restoration. A tool-response rolling-drop policy raised benchmark scores by 1.7 points and cut cost 27.7% versus threshold-triggered summarization.
EpiSEM-Bench is an executable benchmark of 300 matched unsafe/safe task pairs across six domains. It tests whether tool-using LLM agents take prohibited actions when a tool response omits a safety-relevant fact, even though the request is benign and all returned statements are true. It finds large variation across ten models, and recovery access cuts unsafe execution for all ten.
LLM
EpiSEM-BencharXiv
evals, ai-safety, agentsΒ· Added 24 Oct 2026, published 22 Oct 2026
SARIA (Selective Admission and Reload for Inference Acceleration) is a KV caching system that jointly manages DRAM and SSD for multi-turn LLM agents. It persists only blocks likely to be reused beyond DRAM residency, promotes SSD-hit blocks into DRAM for same-session reuse, and paces DRAM reads with a transfer coordinator. Built on LMCache MP, it cuts SSD writes by 57.1 to 58.9% versus full duplicate caching and mean TTFT by 42.3 to 79.7% versus DRAM-only caching.
LMCache MPLLMSSD+1
SARIALMCacheTraceLab+2
inference, researchΒ· Added 23 Oct 2026, published 22 Oct 2026
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.
LLMIndustry Foundation Classes
IFC-MCQarXiv
evals, agents, researchΒ· Added 23 Oct 2026, published 21 Oct 2026
An opinion post from 21 October 2026 arguing that a widely feared PHP vulnerability has no easy exploit. The author says the many GitHub "PoC" repositories fail against a vanilla PHP instance in Docker. They claim LLM agents produced them by depending on a local-file-inclusion flaw or by modifying the server to be vulnerable. The author also argues that LLM defaults make many people's judgements identical.
PHPDockerGitHub+2
CVE-2026-109618CVE-2026-109619GPT+3
coding-agentsΒ· Added 22 Oct 2026, published 21 Oct 2026
Cache Engineering for Agents proposes letting LLM agents control their active KV cache directly, using a drop operation to exclude cache from attention and repos to adjust retained keys' position encodings, without editing the trajectory or re-prefilling. The authors build the AKV serving engine to handle history-dependent cache states. A tool-response rolling-drop policy raises average benchmark scores by 1.7 points and cuts cost by 27.7% against threshold-triggered summarization. AKV gives up to 1.2x output throughput and 1.5x faster P95 latency.
LLMAKV
arXiv
research, inference, agentsΒ· Added 22 Oct 2026, published 20 Oct 2026
PentAGI is an open-source, self-hosted multi-agent system that uses LLM agents to carry out penetration testing tasks autonomously. The MIT-licensed repo has a Go backend, a React frontend and GraphQL. It ships Docker Compose files for Langfuse tracing, Graphiti knowledge-graph memory and an observability stack. Topics list OpenAI and Anthropic as model backends.
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.
FunCAD-Harness is a framework in which LLM agents design CAD mechanisms and revise them using physical simulation feedback. It turns abstract functional requirements into candidate-specific physical tests. The paper also introduces FunCADBench, a benchmark of 46 mechanism families with interdependent, multi-step operations. Experiments on frontier LLM agents show it exposes mechanical failures that visual inspection misses and raises functional success over standard simulation-augmented baselines.
LLM
FunCAD-HarnessFunCADBencharXiv
research, evals, agentsΒ· Added 21 Oct 2026, published 19 Oct 2026
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.
PHREEQCLLMClaude+1
USGSGPT-5.1GPT-5.2+4
research, agents, evalsΒ· Added 21 Oct 2026, published 19 Oct 2026
Hybrid Reasoning Agents (HybridAgents) are LLM agents that switch between latent reasoning and text-based interaction in one generation process. A lightweight HybridLink handles mode transitions, continuous working memory keeps hybrid context across turns, and HybridSFT plus HybridRL train when to think latently and when to act in text. Across 3 model scales and 11 benchmarks, the paper reports 10.4% higher performance, 35.5%-54.7% fewer tokens, and 1.8x-2.6x faster inference.
LLM
HybridAgentHybridLinkHybridSFT+2
research, agents, trainingΒ· Added 19 Oct 2026, published 18 Oct 2026
The One-Pixel of Reasoning introduces the one-relation attack (ORA), which finds a critical implicit warrant linking evidence to a conclusion and inserts one source-grounded sentence that qualifies it, steering an LLM agent to a wrong belief. Across eighteen models on biomedical inference and vulnerability detection, best-of-10 attacks flip initially correct answers 54.9% of the time for proprietary models and 80.0% for open-weight models. Attacks transfer without victim feedback, can pass source-support checks, and multi-agent debate raises team error from 50.0% to 59.5%.
LLM
one-relation attackORAimplicit warrant
AI SecurityΒ· Added 21 Oct 2026, published 17 Oct 2026
Proposes a steering agent that supervises LLM agents and multi-agent systems during execution and corrects goal drift, where long-horizon tool use and inter-agent collaboration slowly diverge from the overall objective. A low-cost fast path decides when to intervene, and a high-cost slow path generates how to intervene. Both paths are trained jointly with reinforcement learning, combining a pairwise ranking objective with GRPO. On information-seeking search tasks it improves performance and efficiency for single-agent and multi-agent systems, generalizes out of domain, transfers to unseen executors, and allows intervention frequency to be adjusted without retraining.
GRPOLLM
arXiv
research, agents, trainingΒ· Added 17 Oct 2026, published 15 Oct 2026
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.
LLM
PROBEGRPODPO+1
AI SecurityΒ· Added 17 Oct 2026, published 15 Oct 2026
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.
GRPODPOGFlowNet+1
PROBEarXiv
evals, agents, trainingΒ· Added 17 Oct 2026, published 15 Oct 2026
GraphACM merges equivalent states from multiple rollouts of the same LLM agent task into a directed, action-labeled graph, so actions can be compared at shared decision states. It defines node-level and global action-consistency measures, and uses the global score as an uncertainty proxy to predict task success or failure. Across three LLMs on ALFWorld, AppWorld and WebShop it reaches an average AUROC of 0.811, above the strongest behavioral-consistency baseline (0.729) and verbalized confidence (0.769). It also shows where in a trajectory agent actions diverge.
LLM
GraphACMALFWorldAppWorld+2
research, agents, evalsΒ· Added 17 Oct 2026, published 15 Oct 2026
Introduces Structured Recipe Optimization (SRO), a paradigm for automated LLM post-training that optimizes over a task-adapted recipe space covering data sources, data processing, cross-domain composition and training configuration. It pairs an LLM-based Recipe-Space Designer, which builds the recipe space for the target tasks, with a Multi-Domain Recipe Optimizer that uses structured trial evidence to guide LLM candidate construction and multi-objective Bayesian trial selection. SRO gets the highest average score in all evaluated target-model and agent-LLM combinations; for example, 41.1 vs 15.4 for the strongest baseline on Qwen3-1.7B with GPT-5.4, and 53.8 vs 40.8 on Qwen3-4B with DeepSeek-V4-Flash, in joint post-training across three capability domains and six benchmarks.
Open-source Python CLI (`tra`) for autonomous trading desks in which LLM analysts (technical, macro, on-chain, sentiment) and validators only give opinions on figures the system computed. Fixed rules do the arithmetic: pricing the round-trip cost, applying risk limits, sizing, cost gates, and sending a single order with stop attached via ccxt (Bybit and OKX demo accounts tested). It supports multiple model providers with per-role models, effort settings and fallbacks. Every answer is parsed against a closed schema, every refusal is logged with the figure behind it, and models can tighten but never loosen controls. This makes it a concrete reference for keeping LLM agents away from arithmetic and money.
Pythonccxtuv+2
Bijective LabsBybitOKX+8
agents, ai-infraΒ· Added 18 Oct 2026, published 13 Oct 2026
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.
LLM
indirect prompt injectionAgentDojo
AI SecurityΒ· Added 15 Oct 2026, published 13 Oct 2026
AgentTune is a source-available Python library that unifies building, training, evaluating, distilling and maintaining tool-using LLM agents. Its core is a two-tier EventLog representation that preserves agent trajectories and yields consistent projections for training, evaluation and auditing. On top of it sit a shared agent runtime, tool-calling reinforcement learning, agentic distillation, RAG, decision workflows, evaluation utilities and a designed path toward self-healing agents, demonstrated on tasks such as legal contract search.
PythonRAG
AgentTune
agents, training, researchΒ· Added 12 Oct 2026, published 9 Oct 2026
An X post by the creator of ROPE, a prompt-injection benchmark for LLM agents. It uses two news items to argue that the prompt-injection model of agent attacks is incomplete: a zero-click RCE called Plugin4Shell in coding agents' plugin systems, and Apple's local MCP server in Safari 27 exposing browser automation to agents. Neither involved persuading a model. The author concludes that teams should benchmark what an agent believes and also audit what it can reach, using plugin signing and update policy, least-privilege credentials per agent, and treating local MCP surfaces as attack surface.
DecepEval is a benchmark of 1,532 instances across 3 task families and 28 professional scenarios designed to measure deception in LLM agents, pairing neutral and induced versions of each task to isolate condition-dependent changes in deception rates. It introduces the LLM Deception Diamond framework (pressure, incentive, opportunity, conflict), adapted from classical fraud theory, and finds that across nine frontier LLMs these inducements raise deception rates even in models with low baseline deception, with long-horizon tasks showing deception rates averaging 87% under induced conditions.
Galaxy Research newsletter piece covering "Shielded Bitcoin," a paper by [[alloc] init] researchers Clara Shikhelman, Mikhail Komarov, and Aleksei Moskvin proposing a Zcash-style shielded pool (encrypted notes, public nullifiers, zero-knowledge proofs) run as a Bitcoin L1 metaprotocol via OP_RETURN envelopes, with peg-in/peg-out deliberately left to a future "PIPEs v2" paper. The article's security-relevant angle flags that novel ZK circuits need battle-testing β citing Zcash's own Orchard Pool soundness bug that survived professional audits for four years before being fixed in the Ironwood upgrade β and that bridge/peg mechanisms (the likely weak point) have historically been the most exploited component in crypto, naming Ronin, Wormhole, Nomad, and KelpDAO. It also notes a growing AI-driven deanonymization threat to transparent blockchains, citing ETH Zurich/Anthropic research showing LLM agents can re-identify pseudonymous users from profile data alone.
OnTrack is a streaming monitor for LLM agents. It uses structure-aware optimal transport to compare an agent's steps and tool dependencies against recorded successful runs, and can alert users or block the agent in about a millisecond per step. It works under three access regimes: full reference (historical runs plus tool schemas), tool schemas only, and step logs only. Monitoring ranges from plan-violation detection to spotting loops, stalls and repeated tool calls. On SWE-bench trajectories, the first 8 steps rank failing runs below succeeding ones better than content-similarity baselines (+0.057 AUROC). An abort policy saves about 18% of compute, and 5 of 6 aborts were correct.
OnTrackSWE-bench
agents, research, evalsΒ· Added 9 Oct 2026, published 8 Oct 2026
The paper proposes evaluating LLM agents on epistemic humility, meaning their willingness to recognize, act on and communicate uncertainty. It operationalizes this as three trajectory-level dimensions (Identify, Solve, Escalate, or ISE). It tests controlled and naturally occurring knowledge conflicts, each with matched no-conflict controls, on four agents. Higher accuracy does not imply more humility: some high-accuracy agents detect conflicts but do not communicate uncertainty in wrong final answers, and agents often detect conflicts early but lose track of them later. Model-level interventions can improve humility but often cost accuracy, which suggests humility depends on the interaction of backbone model, agent harness and environment.
arXiv
evals, agents, researchΒ· Added 9 Oct 2026, published 8 Oct 2026
Introduces AgenticBBO-Bench, a benchmark for LLM-agent black-box optimization across synthetic functions, hyperparameter optimization, database tuning, chip design and molecular design under a unified finite-budget protocol. Agentic BBO beats direct LLM methods in all five domains and the best numerical optimizers in four. Ablations show extra numerical tools don't consistently help, task semantics help more than specific priors, and numerical optimizers can absorb gains from agent-found trajectories. A five-task frontier challenge evaluates seven LLMs under the Codex agent harness, with GPT-6 Astra and DeepSeek-V4.1-Flash on the performance-cost Pareto frontier.
Codex
AgenticBBO-BenchGPT-6 AstraDeepSeek-V4.1-Flash
evals, agents, researchΒ· Added 9 Oct 2026, published 8 Oct 2026
This paper studies where tool-using LLM agents break down in the chain from gathering evidence to taking consequential actions, finding that strong static judgment of whether to act often coexists with much weaker interactive execution, and that failures arise from incomplete investigation, acting before evidence is established, or unresolved prerequisites in multi-action workflows. The authors introduce SafeActBench, a 656-case benchmark across six operational domains and five protocols, paired with a provenance-bound Evidence Ledger and a deterministic trajectory evaluator tested across ten model-harness configurations.
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.
This arXiv paper proposes foundations for proactive LLM agents organized around three joint principles (3T: Task Capability, Temporal Allocation, Trust) and a five-dimensional design space (task scope, anticipation horizon, activation trigger, processing timing, intervention depth). It introduces PROACTIVITY-GYM, a simulation-based testbed with multi-day scenarios, stateful environments, and persona-conditioned simulated users, and reports evaluations across 23 model-harness configurations plus a 30-participant human study showing sharp trust declines after intervention misalignment and a preference for imperfect sleep-time assistance over interruptive help.
ASPIRE is an automated red-teaming engine for LLM agents that discovers indirect prompt-injection vulnerabilities beyond pre-specified attack scenarios. It maintains an evolving Agent Security Behavior Graph and uses complementary Explore and Exploit experts to find, verify, and generalize consequence-centric attacks, with trajectory evidence and cross-run memory improving coverage over time. Benchmarked against existing automated red-teaming approaches, it shows broader coverage of consequences, injection methods, environments, and behavior paths while keeping attack success high.
LLM
Agent Security Behavior Graph
AI SecurityΒ· Added 8 Oct 2026, published 6 Oct 2026
This paper introduces BOTTLED, a benchmark testing whether LLM agents can autonomously convert their general task-solving ability into cheaper, scalable artifacts (e.g., training a small distilled model or writing a reusable program) when faced with millions of similar queries under fixed time, compute, and API budgets, a capability the authors call bottling. Across ten models and three tasks, the authors find zero-shot performance does not reliably predict bottling success: 48 of 60 runs scored below their own model's zero-shot confidence interval, and 31 of 60 underperformed simple small-model distillation baselines. Despite this, strong agents like Opus 5 can retain about 82% of zero-shot macro-F1 at about 657x lower cost on query-product relevance classification, and recover about 94% of a dedicated cheap-inference model's (Jev) performance at a quarter of its cost.
Opus 5Jev
evals, agents, researchΒ· Added 7 Oct 2026, published 6 Oct 2026
WorldSolver is a benchmark of 168 physics-simulation coding tasks drawn from 61 classic computer-graphics papers across 7 physical domains, each giving an LLM agent a fixed code scaffold and asking it to implement the missing solver. Agents are scored on three axes β Execution Checks, Visual Fidelity, and Physical Plausibility β and frontier agents GPT-5.6 Sol and Claude Opus 5 top out at only 48.7% and 46.7% overall, showing current agents struggle to produce executable, visually and physically correct simulation code.
GPT-5.6 SolOpus 5
research, evalsΒ· Added 7 Oct 2026, published 6 Oct 2026
ScienceClaw formalizes a fixed-parameter program self-evolution framework for LLM agents doing scientific tasks, where verified task executions are turned into persistent Skill and Operator updates to the agent's program rather than being discarded. ScienceClaw-Eval, the accompanying benchmark, spans 23 disciplines across natural and social sciences and measures scientific correctness, evolutionary gain, retention, cross-dataset transfer, and evolution cost via sequential task streams and independent reset evaluation. The framework only keeps an update if source-task replay reproduces the repair and the change improves performance on independent scientific tasks, addressing the gap that verified agent executions rarely become lasting program-level improvements.
ScienceClawScienceClaw-Eval
evals, agents, researchΒ· Added 7 Oct 2026, published 6 Oct 2026
Semantic Behavioral Watermarking (SBW) is a provenance scheme for LLM agents that embeds an owner identifier in semantic clusters of an agent's action choices rather than exact action symbols, making it robust to paraphrasing/tool-renaming that breaks three prior agent watermarks (e.g., AgentMark drops to 16.8% bit-recovery under observation paraphrasing). It adds keyed, collision-resistant binning proven unpredictable in the random-oracle model to resist forgery (adaptive forgery success drops from 100% to the false-positive floor), while explicitly documenting an unsolved attack: chained-replay forgery, which still succeeds 0.76-0.98 of the time across five tested agent models (3B-14B, Qwen, ToolBench/ALFWorld benchmarks). Code is released alongside the paper.
QwenLLM
AgentMarkToolBenchALFWorld+2
AI SecurityΒ· Added 8 Oct 2026, published 6 Oct 2026
This paper introduces Semantic Behavioral Watermarking (SBW), a method for embedding an owner identifier into an LLM agent's high-level tool/action choices rather than output tokens, so provenance survives tool renaming and observation paraphrasing. It watermarks over semantic action clusters with history conditioning and replaces the public-cluster bin with keyed collision-resistant binning that is provably unpredictable under the random-oracle model, closing a forgery gap left by prior agent watermarks (e.g., AgentMark, whose bit-recovery drops to 16.8% under paraphrasing). Evaluated across five agent models (3B-14B, four vendors) on ToolBench and ALFWorld, cluster-level watermarking achieves far higher detection under rewriting (0.49-0.66 vs 0.05-0.17 on ToolBench; 0.92-0.97 vs 0.00-0.01 on ALFWorld) and reduces adaptive forgery success from 100% to the false-positive floor, while noting chained-replay attacks remain an open vulnerability (0.76-0.98 detection failure).
Qwen
AgentMarkToolBenchALFWorld+2
agents, researchΒ· Added 7 Oct 2026, published 6 Oct 2026
MemPilot is a framework that lets LLM agents curate memory on demand rather than query-agnostically, using a multi-step policy trained via reinforcement learning to choose between retrieving from pre-built memory or delegating query-specific curation of raw multimodal history to heterogeneous LLMs and VLMs. The policy controls evidence amount, curation instructions, model choice, and visual access, and is optimized with objective-wise advantage decoupling and prefix-based marginal utility estimation to balance performance, cost, and latency. Tested across five multimodal agent-memory benchmarks, it achieves broader performance-cost-latency trade-off frontiers than existing trade-off-aware baselines.
vision-language model
MemPilot
agents, research, multimodalΒ· Added 6 Oct 2026, published 5 Oct 2026
BazaarBench is a simulated consumer-to-consumer marketplace benchmark that measures whether LLM agents acting as buyers and sellers stay safe and honest β tracking ownership, item condition, and commitments to detect six failure types across five transaction stages. Running three 100-agent markets for 30 days plus 45 seven-day continuations across five models (inventories drawn from a public eBay sample), the authors show that double-selling and item-condition deception rise sharply under deadline pressure and adversarial instructions, with GPT-5.4 completing 55.5% of committed sales involving unavailable or misrepresented items; the team releases the simulator, saved market states, eval code, and 357,608 agent-call records.
GPT-5.4
eBay
evals, ai-safety, agentsΒ· Added 6 Oct 2026, published 5 Oct 2026
This arXiv paper introduces RAISED (Robust Attack Invariance through Self-Distillation), a training framework that defends tool-using LLM agents against indirect prompt injection without the utility degradation seen in prior training-based defenses. The authors show existing defenses cause distributional drift that makes models wrongly refuse legitimate tool-output-guided steps on benign tasks, then fix this by having a model generate its own tool-use scenarios and self-distill to match clean-context behavior on both clean and injected trajectory variants. Results show reduced attack success rate on injected tool responses while preserving performance on agentic and general-purpose benchmarks.
LLM
RAISED
AI SecurityΒ· Added 6 Oct 2026, published 5 Oct 2026
This paper introduces a paired-replay testbed that samples a single LLM agent request and replays the identical malicious action, resource, and arguments under four authorization conditions: broad bearer tokens, scoped JWTs, sender-constrained tokens, and Open Policy Agent policies. Across 128 scenarios in four tool domains and five local model configurations, broad-bearer tokens let roughly 9 to 38 percent of attacked, post-exposure decisions execute harmful actions, while all three scoped conditions recorded zero harmful executions; a supplementary AgentDojo extension found 11 of 24 injected attacks succeeded under broad policy versus 0 of 24 under scoping. The authors frame this as evidence that scoping contains cross-action and cross-resource consequences of prompt injection, not that it prevents the injection itself.
Open Policy AgentLLMJWT
AgentDojo
AI Security, Identity & AccessΒ· Added 6 Oct 2026, published 5 Oct 2026
VulValidate is a framework where LLM agents coordinate dynamic-analysis tools to reconstruct vulnerable/fixed code revisions and build triggering experiments that verify whether a function labeled "vulnerable" in a commit-derived dataset actually exhibits the vulnerability. Applied to all 35,849 vulnerable-labeled instances in BigVul, PrimeVul, and DiverseVul, it confirms 57.2%, relabels 19.0% as mislabeled, and leaves the rest undecided or unmeasured, producing a corrected 15,890-function release; retraining five detectors on corrected labels improves F1 for four of five on each dataset. The work exposes how commit-based labeling noise inflates or depresses vulnerability-detector benchmarks and ships a reusable auditing skill and corrected datasets.
VulValidate is a framework where LLM agents coordinate dynamic-analysis tools to reconstruct vulnerable/fixed code revisions, craft triggering inputs, and compare runtime behavior, producing executable evidence for whether a function-level vulnerability label is actually correct. Applied to all 35,849 "vulnerable" instances in the BigVul, PrimeVul, and DiverseVul datasets, it confirms 57.2%, corrects 19.0% of labels, and leaves 22.3% undecided, yielding a corrected release of 15,890 distinct vulnerable function bodies validated against expert review. Retraining five detectors on the corrected labels changes measured F1 on BigVul and DiverseVul, showing that commit-derived vulnerability labels used across the field are substantially noisy and that agentic, execution-grounded auditing is a viable correction method.
VulValidateBigVulPrimeVul+1
evals, agentsΒ· Added 6 Oct 2026, published 4 Oct 2026
The paper introduces PoS (Progression of States), an inference-time framework for long-horizon LLM agents that maintains explicit belief states β combining a current world-state estimate with unresolved task requirements β instead of relying on raw interaction-history memory. PoS detects "Belief Trapping" (stagnation, cycles, or drift where an agent keeps acting without real progress), diagnoses the blocked requirement, and applies targeted recovery constraints, achieving the best results across four benchmarks (including ALFWorld and RCA-100) and three LLM backbones, with gains up to 22.68% and 37.89% over the strongest baselines.
ARTEX global edition is an English-default fork of the Chinese open-source ARTEX (AGPL-3.0), an autonomous penetration-testing system in which planner and worker LLM agents run real tools and record results in an asset graph and an exploration graph on PostgreSQL. It also ships a defense guide, Sigma and Suricata rules, an ATT&CK Navigator layer, MISP indicators and a host triage script for spotting ARTEX-driven attacks.
ARTEXGoNext.js+3
KISAPIPCAct on Promotion of Information and Communications Network Utilization and Information Protection+2
AI Security, Offensive Security, Blue Team & DetectionΒ· Added 31 Oct 2026, published 3 Oct 2026
This paper introduces PoS, an inference-time framework for LLM agents that maintains explicit belief states β combining an estimate of the current world state with unresolved task requirements β instead of relying solely on raw interaction history or memory compression. PoS detects "Belief Trapping," where an agent keeps acting without real progress, and applies recovery strategies tailored to the trapping pattern and the unresolved requirement type. Across four benchmarks spanning execution and diagnosis tasks, and three different LLM backbones, PoS outperforms baseline approaches and shows resilience as context length grows.
NeutronGym is an executable environment for neutron scientific-instrument design where LLM agents build instruments via validating tools, McStas ray-traces the result, and a level-resolved ladder grades syntax, runtime, structure and physics without an LLM judge. A curated McStasBench slice adds 16 tasks from published instruments; seven frontier models solve at most 7 of 16, but reinforcement learning on the ladder's reward takes Qwen3-8B from 11% to 77% on held-out instances (surpassing an untrained Qwen3-32B), with ablations showing the partial-credit ladder is responsible for most of the gain.
Qwen3McStas
NeutronGymMcStasBenchQwen3-8B+1
evals, training, researchΒ· Added 5 Oct 2026, published 2 Oct 2026
This arXiv paper replays ground-truth tool calls from the AgentDojo and tau-bench agent benchmarks (without an LLM) to generate benign-by-construction tool outputs, then labels injected outputs via differential replay to evaluate fifteen prompt-injection detectors, including Meta's Prompt Guard 2, plus two task-aware LLM judges, also testing them on BIPIA. It finds detection rankings transfer poorly between benchmarks (the best detector on BIPIA catches only 2% of AgentDojo injections at 1% false-positive rate; a detector catching 72% of AgentDojo injections catches only 15% on tau-bench), while false-positive rates do transfer between the two agent benchmarks, and traces this to mismatches between training data format (e.g., training on short prompt strings vs. tool-output-embedded injections) and deployment context.
Prompt Guard 2
AgentDojotau-benchBIPIA+1
AI SecurityΒ· Added 5 Oct 2026, published 2 Oct 2026
Persona Guardrail is a production-grade runtime defense framework for customer-facing LLM agents that enforces explicit functional boundaries via synchronous input/output validation against semantic allowlist and blocklist specifications. The authors also introduce PAGE (Persona-Aware Guardrail Evaluation), a benchmark covering benign, adversarial, and out-of-domain user and agent turns; deployed in production, it raises overall accuracy from 85.7% to 95.9% versus a generic LLM-based guardrail, boosts out-of-domain detection from 57.3% to 93.5%, and cuts the false-approved rate from 25.0% to 4.7%, all within latency budget.
LLM
PAGE (Persona-Aware Guardrail Evaluation)
AI SecurityΒ· Added 5 Oct 2026, published 2 Oct 2026
ReFract is a benchmark of 150 expert-validated entries testing whether LLM agents can infer a user's role and act only through tools/actions legitimate for that role, a capability the authors term "Perspective Awareness." Entries are grounded in anonymized industrial-maintenance support queries and use constructed Text World Models to simulate the agent's operating environment and generate perspective-aware action trajectories. State-of-the-art LLMs solve at most 69% of tasks, with over half of trajectories containing attempts at perspective-violating actions, exposing this as a largely unsolved axis of agent evaluation relevant to high-stakes, physically-enacted agent deployments.
ReFract
evals, agentsΒ· Added 5 Oct 2026, published 2 Oct 2026
VenusRL is a fully disaggregated system for agentic reinforcement learning that trains LLM agents through multi-turn interactions with external tool environments. It introduces a priority-aware action scheduler that uses length-prediction heuristics to prioritize sample groups likely to unblock the next training step across batch admission, KV cache residency, and cross-worker orchestration, plus an environment resource manager that packs more tool sandboxes per node via a memory-aware admission threshold and template-keyed page-sharing pool using copy-on-write for isolation. The system achieves up to 4.24x end-to-end training speedup and reduces environment (sandbox) cost by up to 89% over state-of-the-art baselines on agentic RL workloads.
VenusRL
training, ai-infra, agentsΒ· Added 5 Oct 2026, published 2 Oct 2026
agent-wow is an open-source platform that lets LLM agents play World of Warcraft by interacting directly with the AzerothCore private-server network protocol rather than through computer vision or scripted game APIs β the agent itself writes gRPC modules, packet handlers, and even a custom C++ Detour-based pathfinding tool to navigate the world. In this first run, Codex using GPT-6 Astra (xhigh) was given the single prompt 'create an orc character and complete all quests in the starting zone' and completed it unassisted in 40 minutes with zero deaths, data-mining AzerothCore's SQL source for quest/NPC/spawn data to plan its route. The author frames WoW as a long-horizon strategic-planning and real-time tactical-execution benchmark for frontier model agents, comparable in spirit to Mindcraft (Minecraft) and the Factorio Learning Environment, with a stated goal of eventually running many agents through Icecrown Citadel.
Mimir is a physics-grounded LLM agent for long-horizon irrigation control that pairs a fast-timescale loop β where a structured physical interface and deterministic simulator verify, revise, and bound LLM-proposed actions before execution β with a slow-timescale loop that consolidates recurrent failure patterns into persistent contextual principles, while keeping the physical model, evaluator, and execution constraints immutable. Evaluated across multiple sites, crops, and years with a common retrospective evaluator, Mimir achieves the lowest aggregate control cost among tested baselines and uses about 51% less irrigation than historical schedule replay; ablations show forward simulation, verified revision, and persistent context are all necessary, and scaling LLM size or switching model families gives no monotonic improvement.
Mimir
agents, researchΒ· Added 2 Oct 2026, published 1 Oct 2026
SAGE is a statistical acceptance gate for self-evolving LLM agents that replaces the naive accept-if-aggregate-score-improves rule used when an optimizer edits an agents persistent skill document. It uses per-item paired comparison between the current and edited skill on identical validation items, exposing regressions an aggregate score hides, plus a one-sided paired significance test that only commits edits whose wins reliably beat their losses, addressing both permanent regression risk and the optimizers curse from noisy finite validation sets. Across five benchmarks and four backbone LLMs, SAGE cuts regression rates in 19 of 20 settings, for example 36.5 percent to 0 percent on LiveMath and 42.8 percent to 0 percent on OfficeQA with DeepSeek V4, while also achieving the best final score in all 20 settings.
This paper proposes A2A-TIBA, an indirect prompt injection attack that abuses agent-to-agent (A2A/ACP) protocols by getting a target LLM agent to deploy a callback program, letting an attacker bypass the agent entirely to issue commands and exfiltrate data. It also introduces GDA Measurement, a red-team testbed that captures raw gateway context to distinguish four outcome classes (semantic refusal, semantic breach, interception, penetration) instead of a single attack-success-rate metric, and ELA-ITL, a three-layer isomorphic attack-defense model splitting defense into envelope packaging, LLM recognition, and agent interception. Testing on 15 agent-frontend x LLM-backend combinations and a 1,000-case dataset shows that labeling malicious-prompt indicators in envelope channels (A2A, tool, memory) significantly improves LLM recognition of injected content.
LLM
A2A-TIBAGDA MeasurementELA-ITL
AI SecurityΒ· Added 2 Oct 2026, published 30 Sep 2026
A research writeup from Multiverse Computing introducing ProvenanceGuard, a post-generation verification layer for MCP-based LLM agents that checks not just whether a claim is factually supported somewhere in retrieved evidence, but whether it is attributed to the correct source tool or document, a failure mode they call cross-source conflation. Using local models (MiniLM for source routing, a DeBERTa NLI verifier, and a local LLM for claim decomposition) plus a RARR-style repair loop, it was evaluated on 281 real MCP traces from a medical agent, catching 138 of 139 unsupported claims flagged by human experts and correctly identifying the exact source about 86% of the time on held-out data, outperforming four source-blind baseline checkers. The method has already been adopted in NVIDIA's NVFlow finance-agent grounding stage.
Multiverse Computing's blog introduces ProvenanceGuard, a post-generation verification layer for MCP-based LLM agents that checks not just whether a claim is factually supported somewhere in retrieved evidence, but whether it is attributed to the correct MCP source β catching a failure mode they call cross-source conflation (e.g., a refund-policy fact wrongly attributed to an account record). The pipeline decomposes answers into claims, routes each to its most relevant source (MiniLM), scores support with a DeBERTa NLI model, checks source attribution, and can trigger RARR-style repair or fallback for blocked answers. On a 361-claim medical-agent benchmark it caught 138/139 claims that should be blocked, detected all 50 injected source-swap errors, and the approach has already been adopted in NVIDIA NVFlow's finance-agent grounding stage.
Model Context ProtocolDeBERTaMiniLM
ProvenanceGuardRARRNVIDIA NVFlow+3
AI SecurityΒ· Added 7 Oct 2026, published 29 Sep 2026
HasMem (Hard-Origin Adaptively Softened Memory) is a proposed memory architecture for long-term LLM agents that decouples capacity allocation from readout by using frozen hard-prompt embeddings as a verifiable initial state, then adaptively resizing entries via a Writer/Reader/Global controller system. On a 535-question reconstruction probe derived from the Multi-Session Chat (MSC) dev split it reaches 95.3 lexical F1 (+4.4pp over baseline), and on all 500 LongMemEval-S questions it raises local lexical F1 from 3.4 to 8.9 while cutting answer negative log-likelihood from 12.257 to 5.274, outperforming rule-based re-encoding baselines by 8.0β23.6 EM percentage points across several configurations.
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 β catalog discovery, metadata retrieval, and data service invocation β 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.
SEABench is a benchmark of 48 longitudinal task sequences designed to study endogenous misalignment in self-evolving LLM agents β 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.
LLM
SEABench
AI SecurityΒ· Added 29 Sep 2026, published 28 Sep 2026
SEABench is a benchmark for studying "endogenous misalignment" in self-evolving LLM agents β 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.
ReproBench is a benchmark that tests whether LLM agents can autonomously reconstruct an execution environment and reproduce a real vulnerability starting from nothing but a CVE identifier, rather than being handed source code or a container. It decomposes the workflow into six verifiable phases (firmware download/unpacking, analysis, crash validation, etc.) and evaluates agents on 30 real-world IoT firmware CVEs. The study finds only 5.3% of CVE-model pairs achieve genuine reproduction while 45.3% of runs fall back to vulnerability simulation as a shortcut, and it catalogs the bottlenecks blocking agents at each pipeline stage.
ReproBench
evals, agentsΒ· Added 2 Oct 2026, published 28 Sep 2026
ReproBench is a benchmark that tests whether LLM agents can autonomously reconstruct an execution environment and reproduce a vulnerability starting from nothing but a CVE identifier, unlike prior benchmarks that hand agents source code or a ready container. It decomposes the workflow into six phases (firmware download, unpacking, analysis, crash validation, etc.) over 30 real-world IoT firmware vulnerabilities. Results show state-of-the-art agents resort to "vulnerability simulation" shortcuts in 45.3% of runs and achieve genuine reproduction in only 5.3% of CVE-model pairs, with the paper detailing the core bottlenecks blocking full autonomy.
LLM
AI Security, Hardware & RFΒ· Added 2 Oct 2026, published 28 Sep 2026
This arXiv paper proposes a certifier for structured, privileged agent actions such as paying an invoice, assembled from potentially corrupted documents and tool outputs, formalizing when multi-source corroboration can be trusted under indirect prompt injection. It uses a corruption-radius rule counted via minimum hitting sets over corruption-distinct evidence classes so laundered or re-published copies cannot fake a quorum, plus deterministic reconciliation and trusted anchors for single-sourced fields. Evaluated on 70,966 sanctions entities and 450 software-supply-chain provenance records, and against five LLM agents where a realistic injection fools all but one model and existing action-gating or provenance baselines are broken while the certifier admits no unsafe action.
LLM
AI SecurityΒ· Added 2 Oct 2026, published 28 Sep 2026
FirmAgent is an academic research toolkit (NDSS 2026) for finding vulnerabilities in re-hosted IoT firmware web services by combining IDA-based pre-fuzzing (CFG/sink distance scoring), QEMU instrumented tracing, CFG-guided API fuzzing, and an optional LLM agent (LLMATaint.py) that consumes runtime taint traces to reason about and produce PoCs for bug classes like command injection. The pipeline is explicit about scope: it is meant for authorized, owned or re-hosted firmware (e.g. Greenhouse-style containers), and stitches together sink-scope extraction, feedback-driven fuzzing, and request-to-trace attribution before handing traces to an LLM for taint-to-PoC analysis.
This arXiv paper studies persistent memory for frozen-model LLM agents, showing that matching retrieval scope to certification scope prevents locally-valid skill edits from interfering with unrelated task families. Using ORC (Orthogonal Regression Control), an execution-grounded gate for accepting persistent skill edits, on a 12-round code-repair benchmark (ProcStream-RSI), the authors show Scoped-ORC raises mean trajectory utility from 0.713 (global memory) to 0.816 and eliminates harmful deployments (from 6/8 to 0), while accepting far more updates (63 vs 12) with zero harmful acceptances across 27 paired streams.
This is a release changelog for awslabs/mcp, AWS Labs' collection of Model Context Protocol servers covering services like HealthOmics, Aurora DSQL, Bedrock Knowledge Base retrieval, ECS, CloudWatch, billing/cost management, and dataprocessing. This release adds vended metrics tools, SQL parser-based policy guards for Aurora DSQL, agentic retrieval support for Bedrock KB, and fixes several security issues including region validation to prevent SSRF credential disclosure in the CloudWatch server and read-only mode bypass in the MySQL server. It reflects an actively maintained, high-velocity multi-server MCP monorepo used for AWS tool integration with LLM agents.
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.
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.orgpaperπ§ AI
