Agent Goal Hijack

A plan-then-execute agent produces a sequence of steps before acting. If the planner is manipulated, it will emit steps that serve the attacker's goal rather than the user's. Plan-vs-goal validation addresses this by placing an independent validator between the planner and the execution loop: it evaluates each proposed step against the originally-declared goal before the agent is permitted to act on it.

Prompt injection succeeds when untrusted content entering an agent's prompt is indistinguishable from trusted instruction. Three layered techniques address that: spotlighting tags untrusted content with a machine-readable origin mark before it reaches the model; delimiter defence rejects input carrying reserved framework tokens before the model is called; and dual-LLM extraction routes attacker-influenceable content through a quarantined model that holds no tool access, so injected instructions cannot reach the model that can act on them.

An agent that has been compromised, poisoned, or gone rogue will, in most cases, behave differently from its established baseline. Anomaly isolation acts on that difference: when an agent's behaviour score crosses a configured threshold, it is quarantined automatically, credentials revoked, message-queue access cut, in-flight actions aborted. Manual revocation cannot match the speed that cascading multi-agent failures demand.

An agent's behaviour can shift gradually over time: tool-selection patterns change, refusal rates drop, output style drifts. No single interaction reveals it, and a single-shot evaluation cannot catch a trend that spans weeks. Behavioural divergence monitoring detects that drift by comparing per-window statistical distributions of observable agent signals against a declared baseline, and alerting when the gap exceeds a threshold.

An agent exposes more attack surface than a static model: it reasons, plans, selects tools, and acts across multiple turns. Static analysis can characterise that surface, and runtime guardrails can block known-bad patterns, but neither can predict what the agent will do under attacker pressure it has never seen. Behavioural red-teaming addresses that gap through structured adversarial evaluation: probing the agent's reasoning, planning, and tool-use paths with attack strategies before each release.

An LLM processes everything in its context window as a single stream of tokens; it has no innate ability to tell instructions apart from data. If an attacker can place content where the model treats it as instruction, they control the agent. Context isolation prevents that by structurally separating untrusted content from system instructions at prompt construction time, so the boundary is enforced before the model ever sees the input.

An agent's goal can drift across reasoning steps without any single catastrophic event: a manipulated tool output, a planted instruction in retrieved content, or an incremental semantic shift across many planner outputs can each redirect the agent away from its original objective. Goal-consistency monitoring addresses this by persisting the originally-declared goal, deriving a goal-state signal at each reasoning step, and computing a similarity score between the two. When the score falls below a per-task threshold, the monitor pauses the agent and surfaces the divergence for human review before any irreversible action executes.

An agent at a human-in-the-loop gate will be overridden when its decisions do not match the reviewer's judgment. Without a return path, those corrections are discarded: the same miscalibration surfaces again in the next review cycle and the one after that. A feedback loop closes that gap by capturing each override event as a structured record, accumulating those records into a calibration dataset, and using patterns in that dataset to drive targeted changes to the agent's system prompt, tool-scope policy, or divergence-monitor thresholds. A well-calibrated agent produces fewer out-of-distribution decisions, so the review queue contracts over time.

An LLM cannot distinguish data from instructions on its own: that boundary has to be enforced at the point where external content enters the prompt. Input sanitisation does this by normalising, filtering, and structurally segmenting untrusted content before the model ever sees it, so retrieved documents, tool results, and user messages are treated as data rather than commands.

An agent acts on behalf of the user, but nothing in a standard OAuth bearer token records what the user actually approved. If the agent's planning is manipulated, it can invoke tools with parameters the user never sanctioned, while presenting credentials that look valid. Intent attestation fixes this by issuing a short-lived signed token that encodes the exact action and parameter envelope the user authorised, and requiring the resource server to verify that envelope before executing the call.

Agentic systems can act faster than a human can intervene through normal channels. A kill switch is the operational guarantee that a named human role can stop agent activity at any scope (single instance, class, or global) through a documented runbook, without requiring a code change or redeployment, and with every invocation written to an audit trail.

An agent can include links and rich HTML in its output. When that output is attacker-influenced, a clickable link, embedded image, or rich preview card becomes the delivery mechanism for phishing or data exfiltration via markdown image injection. Rendering restriction removes that delivery vector by allowing clickable content only from an explicit allow-list of trusted domains and reducing everything else to plain text before the output reaches the user.

An MCP server response is content the LLM will reason over next. The model cannot distinguish tool output from instruction: that boundary must be enforced at the client, before the payload enters the context window. MCP response sanitisation applies schema validation, Unicode normalisation, control-token stripping, and structural wrapping to every tool result at the response boundary, so adversarial content embedded in a server response cannot redirect the agent's planner.

An AI agent can produce output that is harmful, deceptive, or factually wrong while still sounding fluent and confident. Output moderation places an independent classifier or moderation model between the agent and its destination, checking every output before it reaches a user or a downstream system. The generating model does not evaluate its own answer; a separate gate does.

In a multi-agent system, each agent routes decisions based on what its peers report. If a peer's behaviour becomes unreliable or adversarial, agents that keep treating it with full authority will propagate whatever errors or manipulations that peer introduces. Per-agent trust scoring addresses this by maintaining a continuously updated reputation score for every peer, derived from observed behaviour, and using that score to determine how much authority each incoming message carries.