Attack Based Vulnerability Management (ABVM) is a security methodology that prioritizes vulnerability remediation based on real-world exploitability and attack paths, rather than static severity scores alone. By identifying which vulnerabilities can realistically be used in multi-step attacks to compromise sensitive systems, ABVM enables security teams to focus on what truly matters.
What is Attack Based Vulnerability Management (ABVM)?
Traditional vulnerability management often relies heavily on Common Vulnerability Scoring System (CVSS) scores to guide remediation priorities. However, CVSS does not account for environmental context or how vulnerabilities may interact with each other in a real attack. ABVM shifts the paradigm by evaluating vulnerabilities within the broader context of an organization’s architecture, identity permissions, network exposure, and threat landscape.
ABVM models how attackers might exploit vulnerabilities in sequence—such as moving from a public-facing server to an internal system using chained weaknesses—to reach valuable data or compromise critical assets. This threat-informed approach enables teams to better allocate resources and mitigate the risks that present the most credible danger.
Why ABVM matters
Organizations today are inundated with thousands of vulnerability alerts across sprawling cloud and hybrid environments. With limited time and personnel, security teams often struggle to identify which vulnerabilities warrant immediate attention.
ABVM addresses this challenge by:
- Reducing alert fatigue by deprioritizing vulnerabilities that aren’t exploitable in context
- Highlighting true risks by modeling how attackers move through environments
- Improving remediation efficiency by focusing on realistic attack paths
- Enhancing decision-making by linking technical issues to business impact
This approach provides organizations with a more accurate understanding of their risk posture and helps prevent incidents that stem from overlooked but reachable vulnerabilities.
How ABVM works
ABVM begins by performing comprehensive asset discovery across cloud, on-premises, and hybrid infrastructure. It then analyzes relationships between assets, such as network exposure, trust relationships, identity access paths, and data flow.
Vulnerabilities are evaluated in context—not in isolation. For instance, a medium-severity flaw on an internet-exposed VM that can serve as a beachhead for lateral movement may be more dangerous than a critical CVE on an air-gapped system.
ABVM platforms often use:
- Attack path analysis to determine how threats can propagate
- Risk scoring that accounts for privilege escalation, data access, and exposure
- Continuous updates as infrastructure changes or new threat intel emerges
- Correlation of multiple risk factors (e.g., vulnerable software + weak IAM role + data proximity)
Machine learning may be used to refine prioritization logic and surface critical attack vectors based on historical breach patterns.
Limitations of traditional vulnerability prioritization
Many organizations rely on pre-production static reachability analysis. This typically involves building a dependency tree and flagging all vulnerabilities in direct or transitive packages. While helpful, this creates overwhelming noise and doesn’t reflect what’s actually exploitable in runtime.
Some Software Composition Analysis (SCA) tools go further by identifying which vulnerable packages are invoked in code or even which specific functions tied to CVEs are used. However, this analysis is limited to build-time. It doesn’t provide insight into whether those vulnerable components are actually executed in production.
On the other end, traditional agent-based runtime analysis aims to observe vulnerability execution directly. Yet agents are difficult to deploy at scale, expensive to maintain, and typically limited to individual hosts. As a result, they offer only fragmented visibility.
These gaps leave organizations still struggling with the question: “What vulnerabilities are truly reachable and exploitable in production at scale?”
ABVM best practices
To implement ABVM effectively, organizations should:
- Maintain detailed asset inventories with relationships across environments
- Continuously map network topologies and access paths
- Integrate threat intelligence to stay current on active exploit techniques
- Use attack path analysis to model how multi-step compromises could occur
- Leverage agentless and dynamic reachability analysis tools that can detect the vulnerable packages that attackers can exploit in production environments
- Collaborate across security, IT, and development teams to align on risk priorities
- Incorporate red teaming and penetration testing to validate modeled attack paths
- Establish metrics for success based on risk reduction, not just patch counts
These practices help ensure that ABVM delivers measurable improvements in risk posture and remediation efficiency.
How Orca Security helps
The Orca Cloud Security Platform delivers advanced and comprehensive Attack Based Vulnerability Management across AWS, Azure, Google Cloud, Oracle Cloud, Alibaba Cloud, and Kubernetes environments. Its agentless-first platform surfaces, prioritizes, and helps remediate attack paths based on real cloud context, including:
- Network exposure
- Identity permissions and misconfigurations
- Lateral movement potential
- Proximity to sensitive data
Combining full coverage and comprehensive risk detection, Orca enables security teams to focus on the highest impact vulnerabilities that attackers can exploit. Orca also provides real-time visibility and protection for sensitive workloads using Orca Sensor, a lightweight eBPF-based sensor that detects vulnerable packages actively executed at runtime.
By focusing remediation efforts on reachable and exploitable risks, Orca helps organizations reduce alert fatigue, accelerate MTTR, and strengthen their cloud security posture.
