Agentic AI can think, act, and share information independently. That’s enormously beneficial. But it’s also created a hidden exposure layer – one that isn’t obvious in logs or endpoints but can put systems, data, and workflows at risk.
This concentration has major exposure implications, including:
MCP: Outward exposure to tools and systems
Model Context Protocol (MCP) standardizes how agents interact with external tools and systems. Without MCP, each autonomous agent would need a custom integration for every database. With MCP, agents can discover tools, understand their interfaces, and call them as needed.
Agentic AI is transforming exposure. By adopting exposure management, teams can map the attack paths it creates, understand where risks are greatest, and take control before they’re exploited.
In exposure management terms, the problem is that A2A attack paths don’t look like traditional exploits. Instead of exploiting a vulnerable service, an attacker influences a peer and uses that influence to shift behavior across multiple agents. By the time teams notice, multiple parts of a workflow may have been affected – and the trigger event is buried in context exchanges agents assumed were safe.
- Agent access becomes broad and hard to scope: To avoid breaking workflows, devs often give agents credentials with wide permissions, and those credentials may live longer than the task they were intended for. This creates an attack path where come compromised permission can lead to multiple system breaches.
- Tool metadata becomes part of decision logic: Agents read tool descriptions and structured interfaces as part of how they reason. A malicious or misconfigured tool can therefore influence agent decisions in ways that create exploitable behavior.
- One issue can create many attack paths: Because MCP bridges agents with multiple systems, a misconfigured or compromised integration becomes a hinge point that attackers can exploit.
Across MCP and A2A, four patterns capture how exposure actually manifests:
A2A: Sideways exposure through agent interactions
Two protocols are behind this behavior: MCP, which expands exposure outwards to tools and systems, and A2A, which spreads it sideways across agents. Understanding these differences is key to mapping and managing attack paths before cybercriminals can exploit them.
Exposure management can help manage that risk. It helps security teams:
- Agents treat information from peers as input to decisions without always validating it. That means one misbehaving or compromised agent can pass flawed context to others.
- A2A doesn’t necessary generate a noisy API call or a failed authentication that traditional monitoring would catch. Instead, bad information flows through seemingly normal agent interactions.
- These lateral interactions can form chains of influence before any explicit system call is made – meaning the attack path doesn’t start at an endpoint but at a peer interaction.
This approach turns what could be a hidden, unpredictable risk layer into a manageable and visible part of your security strategy, ensuring you deploy agentic AI safely, at scale, and with confidence.
Traditional security tools were built for predictable interfaces – endpoints, users, and API calls –where access points are known and monitored. But agentic AI has moved risk well beyond those boundaries, requiring more robust AI cybersecurity tools specifically tailored to address agentic risk. Agents make decisions internally, context moves fluidly between tools and peers, and unsafe behavior can emerge without ever hitting a monitored endpoint directly.
Four key exposure patterns in agentic AI workflows
With an exposure management framework in place, teams no longer have to react to every alert. Instead, they connect risks across tools, agents, and workflows, uncovering executable paths an adversary might exploit – not just a pile of isolated findings.
- Over-privileged access chains: Broad agent permissions, combined with tool access or delegated authority, create paths where compromise can affect multiple systems or workflows.
- Context poisoning: Misleading or malicious inputs from tools or agents becomes part of reasoning, generating exploitable paths where agents can act on false assumptions.
- Lateral spread through agent networks: Especially in A2A interactions, flawed context can ripple sideways, propagating errors or unsafe actions across multiple agents.
- Opaque decision chains: Agents make decisions internally based on accumulated context. Limited visibility into reasoning paths hides how exposure occurred, complicating detection and remediation.
In short, MCP expands exposure outward, toward the enterprise systems and sensitive data agents touch. From an exposure management perspective, it raises serious questions: Where are the permissions overly broad? Which tools can agents reach? What attack paths exist from agent access into core systems?
Why does exposure management matter for agentic AI?
Unlike MCP, A2A pushes exposure sideways across agents:
The Agent-to-Agent (A2A) protocol allows agents to share context, delegate tasks, and coordinate without human involvement. For example, one agent might gather information, another might analyze it, and a third might act on it.
- Identify whether issues actually lead to attack paths
- Prioritize them based on likelihood and impact
- Reducing them in a way that meaningfully shrinks exposure
Incorporating exposure management into a comprehensive agentic AI risk management framework matters because, in agentic AI environments:
By understanding these patterns, security teams can map attack paths and prioritize controls, rather than just responding to individual failures.
- Visibility alone isn’t enough: With context flowing dynamically between agents and tools, there are more ways an attacker could influence decisions or behavior than a traditional scan would reveal.
- Exploitability shifts: What looks like a harmless interaction can become a real risk when an agent uses it to pivot into sensitive areas; exposure management helps map those pivot points into attack paths.
- Prioritization becomes practical: Instead of chasing every deviation or flaw, exposure management focuses on the exposures that are accessible and meaningful in your actual environment, aligning security efforts with business risk.
Securing the hidden exposure layer
Put simply, A2A spreads exposure not toward systems or tools, but through the network of agent relationships and shared reasoning.
Think of MCP as USB-C for AI – it replaces a bunch of custom connectors with one universal one. That makes deploying intelligent agents easier and more scalable. But it also concentrates access and trust into a single channel.
By Josh Breaker-Rolfe
