Agent Runtime Security
in/guard/out vs Straiker
Straiker is an agentic security company built around a loop: Ascend AI red-teams your agents for prompt injection, goal hijacking, tool misuse and data exfiltration; Defend AI turns what it finds into runtime guardrails that detect or block those attacks in production; Discover AI maps the agents, MCP servers and tools in your estate. Its Agentic Kill Switch lets a security team take a compromised agent offline in seconds.
in/guard/out starts from a different question. Straiker asks whether a step looks like an attack; in/guard/out asks whether the step is right for this run – within the budget already spent, paying an IBAN the run actually read, keeping screened PII inside – and when it isn’t, it repairs the output or tells the agent how to fix the call.
Side by side
| Capability | Straiker | in/guard/out |
|---|---|---|
| How it decides | Detection models fine-tuned on attack traces, sharpened by its own red-teaming | Deterministic checks on the run’s state first, guard models only as escalation |
| Agent spend | Not described publicly | Hard per-run caps: worst-case cost reserved against a durable ledger before each call |
| A payment’s payee and amount | Tool misuse and unauthorized actions detected | Must appear in something the run read – an injected payee that matches no source is refused |
| Private data | Data exfiltration detected and blocked | Screened to placeholders, restored in the response, and tracked so it can’t leave through a tool argument |
| When a check fails | Detect or block; kill switch for the agent | Repair where safe, or refuse with the problem, the remedy, and whether a retry can succeed |
| Wrong numbers in an answer | Not described publicly | Deterministic grounding: arithmetic recomputed and altered figures corrected from the source |
| Workflow intent | Policies you write | Tool-ordering rules compiled from an n8n export and enforced across the run |
| False positives | “98%+ detection accuracy” claimed | Published: 29 of 32 cases decided correctly on our action-grounding benchmark, refusals of legitimate actions included |
| Getting started | Talk to sales; no public API reference or pricing | OpenAI- and Anthropic-SDK-compatible: a base-URL change |
Based on Straiker’s publicly available information. We’ve done our best to keep it accurate, but details may have changed since we last looked. If you’ve noticed an error, please email us.
An attack detector and a run governor
Detection trained on attacks is strongest where attacks look like attacks: an injected instruction, an exfiltration pattern, a destructive command. Straiker’s loop – red-team the agent, turn every successful attack into a detection – is a good way to build that, and its kill switch is the right tool when an agent is already compromised.
Many of the failures that keep agents switched off don’t look like attacks at all. A loop that has spent most of its budget is calm and well-formed. An invoice the attacker wrote is a perfectly normal document. A customer’s account number leaving through a tool argument is just a string. in/guard/out catches these from run state – what was read, spent and screened – rather than from what the text looks like, and publishes its false positives so you can see what it costs legitimate work.
The two compose. Keep Straiker for red-teaming, discovery and attack detection; point your agents through in/guard/out for run governance and repair.
Frequently asked questions
Is in/guard/out a Straiker alternative?
Partly. Both block prompt injection and data exfiltration at runtime. Straiker adds red-teaming, agent discovery and a kill switch for security teams; in/guard/out adds per-run budgets, payee grounding, PII screen-and-restore, number grounding and repair for the people who own the workflow. Many teams will want both.
Straiker red-teams agents – does in/guard/out?
No. in/guard/out is a runtime layer; it doesn’t generate attacks. It does publish an action-grounding benchmark that scores catches and false positives, and it observes in FIX mode first, so you can measure what it would block on your own traffic before enforcing.
What does repair change for an agent?
A block or a kill switch ends the task. in/guard/out repairs what is safely repairable – malformed JSON, a figure that drifted from its source – and when it refuses a tool call, it returns which argument failed and how to fix it, so the agent can finish the job.
How do I try in/guard/out next to Straiker?
Point your agent’s OpenAI or Anthropic SDK at in/guard/out – a base-URL change – and leave Straiker’s runtime protection where it is. in/guard/out starts in FIX mode, which flags and repairs without blocking.
Catch the attack. Govern the run.
Keep your attack detection and point your agents through in/guard/out - FIX mode observes first, so you can measure what it catches before enforcing anything. We are running a limited demo - sign up and we will get you in as soon as we can.