Security breaches rarely result from a single misstep. Most unfold through a sequence: an attacker compromises a low-privilege asset, escalates access through misconfigured trust or excess permissions, and eventually reaches sensitive systems. These chains of exploitation are often invisible to tools that treat each issue in isolation.

Modern cloud and hybrid environments make this challenge worse. With overlapping identities, dynamic infrastructure, and complex access policies, small oversights can combine in unexpected ways. Attackers look for paths. If defenders do not, they risk missing the full extent of their exposure.

Attack path analysis (APA) addresses this problem by modeling your environment as a graph of users, roles, assets, and permissions. It reveals how access flows between entities and helps uncover the real routes an attacker could take. In this post, we will examine what attack path analysis is, why it matters, and how graph-based tools like PuppyGraph make it easier to find and eliminate high-risk paths before they are exploited.

What is Attack Path Analysis?

Attack path analysis is the practice of identifying and evaluating the ways an attacker could move through your environment to reach sensitive systems. Instead of assessing risks in isolation, it focuses on how a combination of multiple conditions like misconfigurations, weak credentials, and overly broad permissions can form a viable path to compromise.

To understand what makes this approach distinct, it helps to contrast a few closely related concepts:

While attack vectors and surfaces describe how attacks begin or where they may originate, attack paths show how those attacks unfold after entry. They expose the full route an attacker could take, combining access relationships, trust configurations, and reachability into actionable insight.

By shifting focus from individual flaws to the full structure of reachability, attack path analysis gives defenders a more accurate view of risk. In the next sections, we will explore why this matters in real breaches, how graph-based models make it possible, and how to begin applying it in practice.

Why It Matters: Lateral Movement and Real-World Risk

Once an attacker gains access to a system, the breach rarely ends there. They move laterally, navigating across users, machines, and services, until they find a path to sensitive assets or administrative control. This behavior is common in real-world incidents, yet often remains invisible to tools that treat systems and risks in isolation.

Lateral movement is effective because modern environments are complex. Over-permissioned roles, legacy accounts, shared credentials, and misconfigured trust policies create a network of hidden connections. These are not always traditional vulnerabilities, but they form viable links an attacker can exploit. For example, a compromised user with access to a shared internal drive might uncover plaintext credentials that allow access to a privileged server. None of these conditions may trigger alerts on their own, yet together they form a clear path to compromise.

This is not a theoretical concern. In the 2019 Capital One breach, the attacker exploited a misconfigured web application firewall, accessed the cloud metadata API, and used the resulting credentials to extract over 100 million records. In many ransomware campaigns, attackers use tools like Mimikatz or BloodHound to map paths through misconfigured identity systems and escalate to domain admin within minutes.

Attack path analysis helps defenders see these routes before attackers do. By modeling the environment as a connected system, it reveals how unrelated exposures combine into real threats. This offers several key benefits:

• Structural visibility: Teams can see how access flows through the environment, not just where it starts.
  
  
• Prioritized mitigation: Instead of reacting to every isolated risk, security teams can focus on paths that lead to critical assets.
  
  
• Improved detection: Monitoring can be aligned with known high-risk chains, not just individual events.
  
  
• Proactive defense: Attack paths can be disrupted before they’re used, closing off escalation routes that would otherwise remain hidden.

Without this analysis, defenders are left patching surface-level flaws while attackers follow deeper, unseen routes. Attack path analysis gives teams the context to focus their efforts where it matters most.

Examples of Attack Path Scenarios

To see how attack path analysis works in practice, consider two scenarios where small misconfigurations and access decisions form complete routes to compromise. These examples reflect common patterns across cloud and hybrid environments.

Example 1: Developer Account with Overbroad IAM Permissions

A developer’s IAM user is compromised through phishing. The attacker obtains access keys stored in the developer’s environment and logs into the cloud account.

This user has permissions to launch virtual machines and attach roles—privileges granted for convenience during development. The attacker spins up a new VM, assigns it an admin-level role, and retrieves temporary credentials from the instance metadata service.

With those credentials, they begin querying cloud APIs, downloading production data, and modifying firewall rules to expand access. The breach unfolds without exploiting any software vulnerability—only through valid actions enabled by excessive permissions.

A graph-based model exposes this path clearly: compromised developer → role assumption → VM launch → metadata access → privilege escalation.

Example 2: Lateral Movement via Shared Storage and Credential Exposure

An internal user’s VPN credentials are stolen. The account has access to a shared network drive used by multiple departments. The attacker uploads a malicious script to that drive, knowing it will be picked up automatically by a scheduled task running on another host.

When the script executes, the attacker gains access to a second machine, which has cached admin credentials in a configuration file. Using those credentials, they query identity systems and escalate to domain admin, gaining broad control over internal systems.

Here, the path includes shared infrastructure, automated execution, and credential reuse—elements that often fall outside the scope of traditional security tools but are easy to visualize and trace in a graph.

How It Works: A Graph-Based View

Attack path analysis relies on modeling infrastructure as a graph—a structure that reflects how access, trust, and control actually operate across systems. Most environments are already graph-shaped, even if they’re not presented that way. When teams make those relationships explicit, they gain the ability to reason about security in the same way attackers plan their movements.

Modeling Infrastructure as a Graph

Modern infrastructure consists of tightly connected components: users and roles, virtual machines and services, policies and credentials. Graph modeling represents these elements as nodes, and their relationships, such as permission bindings, network flows, or trust connections, as edges. For example:

• A user belongs to a group → the group is attached to a role → the role grants access to a virtual machine.
  
  
• A service account runs on a container → the container connects to a subnet → the subnet has a route to a database.

These links may span across different systems—identity providers, cloud APIs, network layers—and often include transitive relationships that are hard to spot in isolation. A graph brings them together into one connected structure.

Path Discovery with Graph Traversal

Once modeled, the graph can be explored using traversal algorithms to identify potential paths an attacker could take. These include:

• Breadth-first search (BFS): identifies all nodes reachable from a starting point.
  
  
• Depth-limited search: explores access chains while limiting complexity or recursion.
  
  
• Shortest-path algorithms: such as Dijkstra’s, to identify the most direct route to a target.
  
  
• Weighted traversal: allows assigning scores or risk metrics to edges, prioritizing the most dangerous paths.

These techniques help security teams evaluate not just what is exposed, but what is reachable under specific conditions.

Querying the Graph

Graph query languages like Cypher and Gremlin make it possible to express complex security questions declaratively. Teams can define patterns of escalation, search for paths between entities, and apply filters based on environment, privilege level, or asset sensitivity.

MATCH path = (u:User)-[:ASSUMES_ROLE*1..3]->(:Role)-[:CAN_ACCESS]->(s:Secret)
WHERE s.env = "prod" AND u.tags CONTAINS "external"
RETURN path

This query finds all paths from external users to production secrets, allowing for up to three role assumptions. It can be extended to include constraints such as token expiry, trust boundaries, or vulnerability status.

Methodology and Key Steps

Graph modeling provides the structure for attack path analysis, but the real value comes from putting it into practice. This requires more than a graph engine—it demands a workflow that combines discovery, modeling, traversal, and prioritization. When integrated properly, this process gives defenders a consistent and actionable view of how attackers could move across their environment.

Discovery: Gather Data Across Systems

Everything begins with visibility. To build a meaningful graph, you need a broad and accurate inventory of systems, identities, permissions, vulnerabilities, and network relationships. This typically includes pulling metadata from cloud providers to understand IAM roles, trust policies, and resource hierarchies; collecting identity mappings from platforms like Okta or Azure AD; ingesting vulnerability data from scanners like Tenable or Qualys; and incorporating infrastructure knowledge from asset inventories and routing configurations.

The raw data needs to be normalized into a consistent format. This involves resolving identifiers, tagging assets with metadata like environment or business criticality, and reconciling human versus machine identities. Without this normalization, relationships become noisy or misleading.

Modeling: Build the Access Graph

Once you have the data, you can construct the graph itself. Each entity (user, system, token, service account) becomes a node. Connections like role assumptions, group memberships, and network flows are represented as edges. What matters most is capturing both direct and transitive relationships, since many real attack paths rely on indirect access.

This graph should reflect your environment as it truly operates, not just how it is documented. A user might not have explicit access to a production database, but they may assume a role that can reach a container that connects to that database. These chains matter far more than any one permission in isolation.

Enriching the graph with tags (such as production versus development, or high-sensitivity versus low-impact) enables more refined queries. It also lays the groundwork for later steps like prioritization and policy enforcement.

Pathfinding and Prioritization

With the graph in place, defenders can begin exploring it to identify possible attack paths. This starts by defining likely sources, such as external users or developer accounts, and sensitive targets, such as databases or administrative roles. From there, traversal algorithms uncover the possible routes between them.

But identifying paths is not enough. Prioritization is essential. Teams must assess which paths are exploitable in practice, which lead to meaningful impact, and which are exposed to attackers from the outside. For example, a path that includes a patched vulnerability or a temporary session with expired credentials may be less urgent than one involving a persistent misconfiguration that bridges trust zones.

Risk scoring systems can help rank paths, but even without scoring, the structure of the graph enables focused filtering. Defenders can isolate paths that cross environment boundaries, rely on over-permissioned service accounts, or involve lateral movement through shared infrastructure.

Integration and Continuous Use

Attack path analysis is not a one-time assessment. Environments change constantly—new services are deployed, permissions are granted, accounts are created. For this reason, the process must be continuous. Graphs should be updated as new data becomes available, and queries should be re-evaluated regularly.

When integrated into broader security operations, attack path analysis becomes a foundational capability. It helps teams identify where to enforce least privilege, informs red and purple team exercises, guides threat detection strategies, and accelerates incident response by showing how attackers could have moved.

Done well, this methodology turns infrastructure from an opaque sprawl into a navigable map—one that makes the attacker’s options visible, and the defender’s decisions more precise.

Graph Tools for Attack Path Analysis

As we already see, attack path analysis is fundamentally about understanding relationships – how a vulnerability connects to an asset, how user permissions link to sensitive data, how network access allows lateral movement. Traditional security tools often present data in lists or isolated alerts, making it difficult to see these crucial connections. This is where graph technology shines. Attackers inherently think in graphs, mapping out routes through networks and systems. To effectively counter them, defenders must adopt a similar, graph-based perspective.

PuppyGraph: Real-Time Attack Path Insights Without the Overhead

PuppyGraph is the first and only real time, zero-ETL graph query engine in the market, empowering data teams to query existing relational data stores as a unified graph model in under 10 minutes, bypassing traditional graph databases' cost, latency, and maintenance hurdles. Instead of requiring complex and costly processes to move massive amounts of security data (like logs, vulnerability scans, asset inventories, IAM configurations) into a separate graph database, PuppyGraph allows you to query this data directly where it already resides in your existing relational databases (like PostgreSQL, MySQL, Snowflake, etc.).

For Attack Path Analysis, this provides significant advantages:

1. Real-Time Visibility: Query the latest security posture information without waiting for batch ETL jobs. As configurations change or new vulnerabilities are discovered, your analysis reflects reality instantly.
2. Unified View: Seamlessly join data across different sources (e.g., vulnerability data from one table, asset criticality from another, network connectivity from a third) into a single, queryable graph model.
3. Reduced Complexity & Cost: Eliminates the need to build, maintain, and pay for duplicate storage and complex data pipelines dedicated solely to graph analysis.
4. Scalability & Performance:PuppyGraph delivers exceptional query performance and robust scalability, essential for complex attack path analysis. Its distributed architecture separates computation and storage, efficiently handling petabyte-scale datasets. By leveraging techniques like vectorized processing and optimized data access, PuppyGraph provides rapid responses to intricate queries, ensuring you can analyze vast amounts of security data quickly and effectively.  
5. Interactive Visualization: Explore potential attack paths visually, making complex chains of exploits and permissions easy to understand and communicate.

With PuppyGraph, security teams can rapidly model potential attack paths, ask sophisticated questions about reachability ("Can this internet-facing vulnerability lead to our production database?"), identify choke points, and prioritize remediation efforts based on actual connections and exposures, all leveraging their existing data infrastructure.

The Broader Landscape of Graph-Based APA Tools

The power of graphs for APA is increasingly recognized across the industry:

• Integrated Security Platforms (CNAPP, Exposure Management): Many modern platforms like Wiz, Orca Security, Tenable One, and Microsoft Security Exposure Management incorporate graph visualizations. They leverage internal graph structures to correlate alerts, map cloud assets and permissions, and show potential attack paths within their specific ecosystems. While powerful, they often operate within their own data models.
• Traditional Graph Databases: Platforms like Neo4j have long been used to build dedicated cybersecurity knowledge graphs. These are highly capable but typically require significant effort in data modeling, ETL development, and infrastructure management to centralize security data before analysis can begin.
• Specialized & Open Source Tools: Niche tools also leverage graph concepts effectively. BloodHound is an excellent open-source example focused specifically on visualizing and querying attack paths within Active Directory environments. Visualization tools like Maltego help analysts manually or semi-automatically build graphs from diverse data sources for investigation.

Conclusion

Attackers succeed not because of a single weakness, but because systems are full of invisible links: overlapping permissions, indirect trust, and overlooked misconfigurations that no one thought to trace. Attack path analysis turns these hidden relationships into something you can see, understand, and act on.

By modeling infrastructure as a graph, teams gain a clearer picture of how access truly flows across their environment. They can trace escalation paths, isolate high-risk identities, and prioritize the exposures that matter most. Whether through specialized tools or real-time engines like PuppyGraph, this approach shifts the advantage back to defenders—making complex environments more understandable, and real threats more preventable.

If you’re ready to explore how graph analytics can reshape your security strategy, try the forever-free Developer Edition or book a demo with our team.