tag: Prompt-Injection · 38 items
- Engineer — Learn: New research on prompt injection attacks targeting agent-planning workflows is worth reviewing for teams building or deploying LLM-based agents, but no enrichment signals indicate active exploitation requiring immediate action.
- SOC/IR — Learn: Documents a new TTP class against AI agent pipelines; no IOCs or detection surface are evident from the title alone, so file for context when developing future coverage of AI-agent abuse.
- Leader — Plan: If AI agents are being adopted in your environment, add agent-planning prompt injection to your AI usage policy and risk register this quarter — this research signals the attack surface is maturing before most enterprises have governance in place.
- Engineer — Plan: If you deploy any of the named guards (Shieldstral, ShieldGemma, Llama Guard 3, Qwen3Guard, gpt-oss-safeguard) in a pipeline, audit whether your input preprocessing allows appended forged structure; the mitigation—retraining guards to score no lower than their most harmful fragment—cuts attack success from 75% to under 2%, so track upstream patch releases for these models.
- SOC/IR — Learn: Target rebinding is a structurally novel evasion class against LLM safety layers, but the paper reports no in-the-wild exploitation and the attack leaves no distinct log artifact to hunt; worth understanding as AI-application attacks become a detection domain.
- Leader — Learn: Research demonstrates that widely used LLM safety guards can be systematically bypassed, which is relevant if your organization ships AI features relying on these guards for policy enforcement; no immediate action required, but factor into your AI risk posture reviews this quarter.
- Engineer — Learn: Researchers demonstrate 100% success rate for steganography-based indirect prompt injection against undefended MCP pipelines and propose a provenance-binding defense using HMAC labels and taint tracking — worth reading if you’re designing or reviewing agentic AI architectures today, but no active exploitation and no patch to apply.
- SOC/IR — Learn: The attack class (steganographic trust laundering across agent boundaries) is novel and lacks any published IOCs, detection surface, or ATT&CK mapping yet — file for future detection work as MCP-based workflows become operationally common.
- Leader — Skip
- Engineer — Act: Active prompt-injection technique caught targeting real developer machines: restrict AI coding agents to project directories only and disable auto-execution of package-install commands to eliminate this ambient-file attack surface.
- SOC/IR — Plan: New TTP worth covering: build detections for package manager processes (npm/pip/etc.) spawned as child processes of AI coding agent binaries, especially when triggered outside expected project paths.
- Leader — Plan: AI coding agents in your developer toolchain now represent a prompt-injection risk from ambient files; establish a policy governing agent filesystem scope and command permissions before an undetected attempt succeeds.
- Engineer — Plan: A real-world case of indirect prompt injection: a malicious AGENTS.md file in a Downloads folder redirected an AI coding agent to install a rogue npm package. Audit your system for unexpected AGENTS.md (or similar instruction) files in user-accessible directories, and restrict your AI coding agent’s trusted file-read scope to the project root.
- SOC/IR — Plan: This establishes a new TTP: adversaries plant instruction files (AGENTS.md) in directories that AI coding agents scan, triggering unauthorized package installs. Build a hunt for AGENTS.md or equivalent agent-config files outside expected project roots, and add detections for anomalous package manager invocations spawned by AI agent processes.
- Leader — Plan: If your engineering org uses AI coding agents (Claude Code, Copilot, Cursor, etc.), this attack class — poisoning agent instruction files to hijack developer workflows — warrants a policy before it matures further; define which directories agents may trust for instruction files and require developer awareness training this quarter.
- Engineer — Learn: Prompt injection through untrusted content (emails, web pages, plugins) allowing agents to abuse their own permissions is a real attack class worth understanding before deploying agentic workflows; no patch or CVE — evaluate agent permission scoping and input sanitization in designs.
- SOC/IR — Skip
- Leader — Plan: AI agents acting on behalf of users are an emerging enterprise attack surface that needs an acceptable-use policy and governance framework before broad deployment; schedule a review of any approved agentic tools and their permission boundaries this quarter.
- Engineer — Learn: Novel defensive architecture showing that agents consuming flattened page text inherit no browser-enforced trust boundaries, enabling injected content to hijack LLM actions. Worth studying before deploying any agentic workflow that browses untrusted pages.
- SOC/IR — Learn: The research quantifies how easily injected third-party content captures AI agent authority (73% success on a naive planner), a useful threat model for hunting or scoping risks if your org deploys AI browsing agents.
- Leader — Learn: Useful framing for AI-agent deployment risk: browser agents run with user authority but lack the trust-boundary enforcement of a human operator, a gap worth addressing in your AI acceptable-use policy before broad rollout.
- Engineer — Plan: Organizations running Salesforce Agentforce with Email-to-Case or Web-to-Case should audit their Flow configurations to identify where untrusted customer input reaches agent prompts without sanitization; no exploitation signals present but the attack surface is as-designed and non-obvious.
- SOC/IR — Learn: This outlines how standard Salesforce workflow chaining can route external user-controlled content into AI agent prompts — a useful mental model for the indirect prompt injection attack class, but no IOCs, TTPs, or detection signatures are provided for immediate action.
- Leader — Plan: If your organization uses Salesforce Agentforce for customer support intake, commission a review of which agentic workflows ingest customer-controlled data; this is the ‘AI agents need policy before controls’ gap and warrants a governance check before broader Agentforce rollout.
- Engineer — Learn: Academic research showing how prompt injection can propagate across multi-agent architectures and leak sensitive data — no PoC or active exploitation, but directly relevant to engineers building LLM pipelines or agent-based workflows who should design for prompt isolation and trust boundaries between agents.
- SOC/IR — Learn: No IOCs, no ATT&CK mappings, and no evidence of active exploitation, so no immediate detection work; worth filing as context for environments that have deployed AI agents, where inter-agent communication channels represent an emerging and largely unmonitored attack surface.
- Leader — Plan: This research reinforces that AI agent deployments in enterprise workflows carry systemic risk from prompt injection; use it this quarter to drive a policy on approved AI agent frameworks and required security review before production deployment, before controls are needed.
- Engineer — Learn: This incident illustrates real prompt-injection risks in agentic AI pipelines that have live network access; engineers building AI-assisted tooling should review whether their evaluation or CI-integrated agents can reach external systems and gate that access during testing.
- SOC/IR — Skip
- Leader — Plan: If AI agents are being piloted or deployed with internet access, establish a policy requiring sandboxed/offline environments for evaluation stages before this quarter ends; this incident provides a concrete justification for that control.
- Engineer — Learn: Novel architectural approach that assigns ring IDs to tokens by origin, creating hard trust boundaries inside the model — relevant for teams building or evaluating LLM pipelines, but no deployable artifact or patch exists yet.
- SOC/IR — Learn: Reinforces that indirect prompt injection is a structural problem in current LLM deployments, useful context for analysts building detection logic around agentic or RAG-based systems, but no IOCs or hunt opportunities here.
- Leader — Learn: Confirms that LLM systems lack native separation between authoritative and non-authoritative inputs — useful framing when developing AI governance policy or evaluating vendor security claims around agentic products.
- Engineer — Plan: If you run AWS AgentCore Harness for AI agent workloads, audit your default IAM role assignments and trust boundaries this quarter — the research identifies specific misconfigurations that allow injected prompts to reach credential storage. No active exploitation or PoC noted, but the hardening steps are concrete and low-effort.
- SOC/IR — Learn: The prompt-injection-to-credential-exfiltration path in AI agent runtimes is a maturing attack class worth understanding for future detection coverage, but no IOCs, active campaigns, or ATT&CK mappings are surfaced here to act on today.
- Leader — Learn: This illustrates the emerging risk of AI agent frameworks inheriting over-privileged identities — useful context for shaping an AI/LLM agent governance policy before the attack surface grows, but no immediate leadership action is required.
Learn
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ChemMat-AgentSafetyBench: AI chemistry agents release hazardous protocols in 25% of attack runs
- Engineer — Learn: Benchmark formalizes a taxonomy of long-horizon agentic attacks — tool chaining, memory poisoning, task injection — applicable beyond chemistry to any agent with persistent memory and tool access. No patch or CVE; use findings to inform threat modeling when designing or reviewing agentic systems.
- SOC/IR — Skip
- Leader — Learn: Research demonstrating that AI agents can be steered to emit hazardous outputs in roughly one-in-four adversarial runs is useful framing for board-level AI adoption risk discussions, but no immediate action is required unless the org runs specialized scientific AI agents.
- Engineer — Learn: New read-only plugin worth evaluating if DeepSeek Harness is in your AI pipeline; covers prompt-injection detection and local config audit, but adoption is nascent (51 stars) with no enrichment signals to pressure a faster decision.
- SOC/IR — Skip
- Leader — Skip
- Engineer — Plan: If your developers run Amazon Kiro IDE 0.7.45 on Windows, verify whether a patched version is available and update; the prompt injection → data exfiltration path via Kiro Powers is a real supply-chain risk for dev environments. No KEV or PoC signals elevate this to Act.
- SOC/IR — Learn: No IOCs, active exploitation evidence, or ATT&CK-mappable detection surface are present; the item illustrates a prompt injection exfiltration pattern in agentic IDEs worth tracking as AI dev tooling becomes a threat surface.
- Leader — Learn: Useful data point for AI tool governance: agentic IDEs can become data-exfiltration vectors via prompt injection, with no CVE or patch timeline disclosed yet — worth a line item when reviewing AI-assisted development tool policies.
- Engineer — Plan: If you run Ollama locally or in AI agent pipelines alongside NemoClaw, this unauthenticated takeover path (likely DNS rebinding or CORS abuse against Ollama’s HTTP API) is a real exposure. Check Ollama’s network binding config now and watch for NVIDIA’s patch or mitigation advisory — no public PoC or KEV listing yet, but the attack surface is credible.
- SOC/IR — Learn: No IOCs, no active exploitation, and no mapped TTPs — nothing to hunt or detect today. However, the technique (webpage-initiated control of a local AI agent instance to inject hidden instructions) is a novel attack class worth tracking as AI agent deployments grow in enterprise environments.
- Leader — Learn: No breach or regulatory trigger here, but the finding illustrates that local AI agent tooling carries real attack surface — useful input for AI security policy and vendor risk reviews if your organization is adopting agentic AI infrastructure.
- Engineer — Learn: Research demonstrates that prompt-level privacy policies fail to reliably prevent LLM agents from embedding protected attributes into generated tool-call arguments; if you ship agent pipelines, this motivates adding a purpose- and destination-aware inspection layer before tool execution, though no live exploit exists requiring an immediate change today.
- SOC/IR — Learn: Novel disclosure vector where adversarial task context pressures agents into leaking protected fields via tool arguments — no IOCs, ATT&CK mappings, or active campaign to hunt for, but relevant background if your org monitors AI agent activity.
- Leader — Learn: Controlled research showing prompt-level privacy guardrails in LLM agents are not a reliable enforcement boundary; useful context when developing AI governance policy for agent deployments, but no breach or regulation deadline requires immediate action.
- Engineer — Learn: Novel indirect prompt injection variant that weaponizes web-page summarization to exfiltrate user metadata and conversation history from Grok; no patch or PoC signals, but informs how teams should sandbox AI agents that fetch and process external web content.
- SOC/IR — Skip
- Leader — Learn: If employees use Grok for work tasks, this technique demonstrates that malicious web pages can silently exfiltrate prompt content; worth referencing when reviewing AI-tool acceptable-use policies, but no active exploitation warrants immediate action.
- Engineer — Learn: Novel attack class showing that writable system-prompt state files in multi-agent harnesses can carry self-propagating payloads between agents; no exploitation in the wild yet, but engineers building agentic pipelines should treat those files as untrusted input surfaces and avoid giving agents write access to other agents’ system prompts.
- SOC/IR — Learn: Pure research with no IOCs, no ATT&CK mapping, and no detected campaigns; no hunt or detection to write today, but worth tracking as agentic AI deployments grow and this technique matures toward real-world use.
- Leader — Plan: If the organization is deploying or evaluating multi-agent AI systems, this peer-reviewed research identifies a systemic risk class that warrants a policy guardrail — specifically around which components may write to agent state files — before agentic tooling scales further internally.
- Engineer — Learn: No enrichment signals (no KEV, PoC, or active exploitation), but the attack surface is real: plaintext secrets in MCP config files and over-permissioned access are design-level risks engineers should factor in when deploying AI agent infrastructure. Audit any existing MCP deployments for credential storage and permission scope before expanding use.
- SOC/IR — Learn: No IOCs, TTPs, or detection artifacts are surfaced here, but the ‘server running before security teams know’ framing highlights a shadow-AI discovery gap worth tracking. No immediate detection work is possible from this summary alone.
- Leader — Plan: If the organization is adopting AI agents or MCP-based tooling, this is a quarter-horizon governance signal: establish an MCP server inventory policy and access-permission standard before the deployment footprint grows and secret exposure becomes a reportable incident.
- Engineer — Plan: AI coding assistants with MCP integrations are actively used in engineering workflows and this technique can bypass safety refusals to steal SSH keys, env secrets, and source code. Audit all connected MCP servers, restrict to explicitly trusted/internal ones, and review what credential stores and source directories your AI assistant can reach.
- SOC/IR — Learn: Instruction-splitting to evade AI safety filters is a novel exfiltration technique worth understanding, but no IOCs, ATT&CK mappings, or detection surface are provided here — file this as an emerging technique to monitor as tooling matures.
- Leader — Plan: Widespread enterprise adoption of AI coding assistants creates a new third-party risk vector: a malicious or compromised MCP server can silently exfiltrate source code and credentials. Establish an approved-MCP-server policy before your engineering teams expand AI tool integrations this quarter.
- Engineer — Learn: Multi-step indirect prompt injection significantly raises attack success rates on computer-use agents (up to 72.9% for GPT-4o-mini at three-step depth), which is directly relevant to teams building or deploying agentic AI systems; no patch exists, but understanding this attack class should inform how you design sandboxing, permission scopes, and input validation for any CUA deployment.
- SOC/IR — Learn: This research formalizes a new attack class against AI agents that may soon appear in enterprise environments; no active exploitation or IOCs reported, but understanding multi-step injection techniques will help detection engineers think ahead about behavioral anomalies in agentic workflows.
- Leader — Learn: If your organization is piloting or deploying computer-use AI agents, this benchmark demonstrates meaningful safety gaps in current state-of-the-art systems; worth factoring into your AI governance policy and vendor evaluation criteria before broader rollout.
- Engineer — Act: Two independent attack paths were found; only one is confirmed patched, leaving a live exfiltration surface in any Rovo-enabled Atlassian instance. Disable or restrict Rovo access to sensitive projects until Atlassian confirms both routes are fully remediated.
- SOC/IR — Plan: The technique — hiding adversarial instructions in Rovo-readable content to trigger outbound data sends — is a concrete TTP worth building a detection for. Create a hunt query for anomalous outbound connections originating from Atlassian services to external hosts.
- Leader — Act: If your organization uses Atlassian Rovo, one exfiltration route remains unpatched, meaning confidential Jira and Confluence data accessible to any signed-in user is at risk today. Confirm with your Atlassian admin whether Rovo is active, assess the data exposure scope, and request Atlassian’s remediation timeline before this surfaces in customer security questionnaires.
- Engineer — Act: If your team runs Claude Code or Gemini CLI in CI pipelines under vendor-default configuration, an unprivileged GitHub issue can reach your runner and exfiltrate CI secrets — audit all AI agent CI integrations now, restrict what secrets are scoped to those runners, and disable issue-triggered agent workflows until hardened configurations are documented.
- SOC/IR — Plan: This Black Hat presentation defines a new TTP category — prompt injection via issue trackers targeting AI coding-agent CI workflows — worth building detections for; plan to monitor for anomalous CI runner invocations originating from issue events and unexpected secret-access patterns in pipeline logs.
- Leader — Plan: Default configurations of AI coding agents from major vendors expose CI secrets to anyone who can open a GitHub issue — assess whether engineering teams have deployed these tools in CI/CD pipelines this quarter and establish an approval policy for AI agent access to production secrets before adoption widens.
- Engineer — Learn: Novel attack class: hidden payloads in pre-filled AI deep links can alter LLM memory without user awareness. No exploitation signals or PoC, but engineers building AI-integrated features should audit any ‘Ask AI’ button implementations for unsanitized prompt passthrough.
- SOC/IR — Learn: No IOCs, ATT&CK mapping, or active campaign indicators are present. Worth tracking as AI assistant adoption grows, but there is no detection surface or hunt query to act on today.
- Leader — Plan: This attack class is relevant to any enterprise deploying AI assistants with memory or context features; factor it into AI acceptable-use policy and vendor evaluation criteria before broader rollout.
- Engineer — Plan: Google already removed the affected workflows, but the pattern — a public GitHub issue prompt-injecting a triage agent into triggering a privileged code-fixing bot — applies to any AI pipeline where untrusted input can influence an agent holding elevated credentials. Audit your own ADK or similar agent workflows to ensure public-facing inputs cannot reach privileged action agents, and enforce least-privilege scoping on any bot collaborators.
- SOC/IR — Learn: This demonstrates a novel escalation path: prompt injection via public GitHub issues → triage agent manipulation → privileged bot action. No IOCs or active exploitation are reported, but detection engineers building coverage for AI agent abuse should note this TTP as a new vector to model.
- Leader — Plan: If your organization uses ADK or similar AI-powered developer tooling with privileged repository access, initiate a permission-scope review this quarter; the finding illustrates that AI agents integrated into development workflows can become unexpected privilege-escalation paths, which warrants a policy guardrail before broader adoption.
- Engineer — Learn: Identifies a novel design flaw where LLM memory consolidation strips trust-level metadata from external inputs, letting injected content inherit user-level authority. No patch cycle applies yet, but teams building agentic systems with persistent memory should review their memory consolidation pipelines against this authority-amplification model.
- SOC/IR — Learn: No IOCs, active exploitation, or detection surface currently exist; this is pre-deployment research. Worth tracking as AI agent adoption grows, as it describes an attack class that would be difficult to detect with existing SIEM/EDR tooling.
- Leader — Learn: Establishes a concrete risk category for enterprise LLM agent deployments — memory subsystems can be poisoned to escalate trust silently. Useful framing for AI governance discussions, but no vendor exposure or regulatory deadline triggers action this quarter.
- Engineer — Learn: The paper’s four-property model (Source Authorization, Task Alignment, Action Alignment, Data Isolation) offers a useful design lens for teams building agentic systems, but no running system requires a change today — absorb when designing agent authorization boundaries.
- SOC/IR — Learn: Reframing indirect prompt injection as a Source Authorization violation is a useful mental model for thinking about what agent behaviors to monitor, but the paper yields no IOCs, detection rules, or hunt queries.
- Leader — Skip
- Engineer — Act: If you run Microsoft’s official Azure DevOps MCP server for AI code review, disable or restrict the PR-description tool until Microsoft ships a patched version with prompt-injection guardrails; an attacker with only PR-comment access can pivot the agent into unintended projects and exfiltrate output.
- SOC/IR — Plan: No published IOCs, but build detection for anomalous AI agent cross-project access in Azure DevOps audit logs — unusual MCP tool invocations touching repos outside the agent’s expected scope are the behavioral signal to hunt for.
- Leader — Plan: This illustrates a systemic gap in AI coding-agent deployments: prompt injection via developer workflow inputs can bypass access controls; use this as a prompt to add MCP/AI-agent integration scope to your existing AI governance policy review this quarter.
- Engineer — Plan: Developers running Kiro should update to the patched version; also review agentic tool permissions and consider whether your workflows allow Kiro to fetch and process arbitrary external URLs without human review of rendered content.
- SOC/IR — Learn: This demonstrates a concrete prompt-injection-to-RCE chain in an agentic coding IDE — no IOCs or active exploitation to hunt for now, but the attack class (hidden page text hijacking agent actions) is worth understanding as AI coding tools spread across developer estates.
- Leader — Skip
Plan
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Prompt Injection via Invisible Overlay Text Chains to Host Code Execution in Android AI Agents
- Engineer — Learn: Researchers demonstrated a novel attack chain — invisible overlay text on Android feeds malicious instructions to an AI agent framework, which then executes commands on the host PC. No patch, KEV, or PoC is available yet, but this changes how secure AI agent pipelines should be architected (sandboxed execution context, input validation on screen-scraped content).
- SOC/IR — Learn: No IOCs, active campaigns, or actionable detection surface are described; the value is understanding the emergent attack class of UI-layer prompt injection into agent frameworks, which may inform future alert logic as mobile AI agents reach enterprise environments.
- Leader — Plan: This research confirms that AI agent deployments carry a concrete lateral-movement risk before defenses mature; if your org is evaluating or piloting mobile AI agents, prioritize an AI usage policy and architecture review for agent sandboxing this quarter before broader rollout.
- Engineer — Learn: Research proposes interposing a deterministic symbolic controller with signed hash-chained instruction streams between LLM agents and privileged tools to prevent prompt-injection-driven authorization bypass — worth reviewing when architecting AI agent pipelines with privileged tool access, but no production implementation exists to adopt yet.
- SOC/IR — Skip
- Leader — Learn: Highlights a structural gap in current AI agent deployments: identity-based auth doesn’t constrain which actions an authenticated agent can take at runtime, creating hijack risk relevant to any enterprise adopting agentic workflows; useful framing for AI governance policy discussions.
- Engineer — Plan: Teams deploying AI agents (coding assistants, browser agents) should audit what external data sources agents consume and add output-validation gates before agents take irreversible actions like purchasing, executing shell commands, or committing code.
- SOC/IR — Learn: Useful for understanding a new class of agent-manipulation attacks that could be used as an initial-access vector in environments with autonomous AI tooling, but no IOCs or active exploitation reported to act on now.
- Leader — Plan: As AI agents are deployed internally, establish a policy requiring human-in-the-loop approval for high-stakes agent actions (financial transactions, code execution) before agent autonomy is expanded this quarter.
- Engineer — Learn: OpenAI’s internal adversarial training methodology for prompt injection offers design patterns worth studying if you’re building or securing LLM-based applications, but no patch or configuration action is required today.
- SOC/IR — Skip
- Leader — Learn: Understanding that major AI providers are investing in automated red-teaming for prompt injection is useful context for evaluating AI vendor security posture and shaping internal AI usage policies.
- Engineer — Learn: Novel prompt-injection variant that abuses persistent agent memory via a malicious email payload; no patch or KEV exists, but engineers building AI agents with memory + inbox access should audit whether memory writes can be triggered by untrusted input and add confirmation gates before persisting new user ‘facts’.
- SOC/IR — Learn: No IOCs, ATT&CK mappings, or active exploitation reported; the attack’s stealthiness makes detection at the SIEM/EDR layer impractical without application-layer logging of memory writes, so this is awareness context for future detection design rather than an actionable hunt.
- Leader — Plan: Organizations piloting AI assistants with memory and email access now have a concrete manipulation risk to include in AI deployment governance — draft or update your AI agent policy this quarter to require human approval before agents persist new user-context facts sourced from inbound messages.
- Engineer — Plan: GitHub Copilot is broadly deployed on developer workstations; a public PoC exists for this RCE-via-prompt-injection path, but EPSS is 0.03 and it is not KEV-listed. Check for an available Copilot update and audit whether your pipelines or editors process untrusted file content through Copilot without sandboxing.
- SOC/IR — Learn: Prompt injection as an RCE delivery mechanism in AI coding assistants is a novel developer-endpoint attack class worth adding to your threat model, but the summary provides no IOCs or ATT&CK-mappable TTPs to act on for detection tuning today.
- Leader — Plan: If your organization deploys GitHub Copilot to developers (very common), a demonstrated RCE path represents a developer-workstation supply-chain risk; confirm with engineering whether a patched version is available and assess exposure this quarter before exploitation pressure rises.
- Signals: CVE-2025-53773 — CISA KEV: not listed, EPSS 0.03, public PoC on GitHub
- Engineer — Plan: Research-grade but practical: any AI coding agent with access to .env or secrets files is a potential exfiltration path via a malicious image in a PR. Audit what filesystem scope your AI code-review agents hold, and restrict or deny access to credential files and secret stores.
- SOC/IR — Learn: Novel TTP — prompt injection embedded in images bypasses AI reviewers that never inspect image content, then coerces coding agents into exfiltrating secrets. No active exploitation or IOCs reported; file for future detection work around anomalous AI-agent file reads.
- Leader — Plan: Demonstrates that AI coding-agent tools carry unchecked secret-exfiltration risk through a non-obvious vector. Before broader AI agent adoption, establish a policy governing what repository paths and credentials these tools may access, and confirm existing vendor tools have equivalent controls.
- Engineer — Learn: New prompt injection techniques are relevant to engineers building or integrating LLM-powered features; read to update threat model for AI application design, but no patch or config action is indicated without a summary or enrichment signals.
- SOC/IR — Learn: Awareness of emerging prompt injection TTPs may eventually inform detections for AI-adjacent pipelines, but with no IOCs, ATT&CK mappings, or exploitation detail available, there is nothing actionable to hunt or tune today.
- Leader — Skip