Malik Haidar is a veteran cybersecurity strategist who has spent years fortifying the digital perimeters of multinational corporations against sophisticated threat actors. With a deep background in identity access management and threat intelligence, he specializes in bridging the gap between high-speed business innovation and robust security protocols. As organizations rush to integrate Large Language Models (LLMs) into their core operations, Malik’s work focuses on the often-overlooked vulnerabilities within the infrastructure that powers these models, particularly the explosion of non-human identities and exposed endpoints.
The following discussion explores the critical risks associated with LLM infrastructure, where rapid deployment often outpaces security oversight. Malik breaks down how internal APIs and administrative dashboards become unintended gateways for attackers, the inherent dangers of static non-human credentials, and why the “internal-only” mindset is a primary driver of modern breaches. He provides a practical roadmap for implementing zero-trust principles, moving beyond simple perimeter defense to a strategy of limiting the impact of an inevitable compromise through automated secret rotation and least-privilege enforcement.
In modern infrastructure, LLM endpoints serve as the interface for users, applications, and external databases. How do inference APIs differ from administrative dashboards in terms of risk, and what common oversights lead to these interfaces becoming unexpectedly reachable from the public internet?
Inference APIs and administrative dashboards present two very different types of “keys to the kingdom.” An inference API is the workhorse—it handles prompts and generates outputs, but because it often connects to external plugins and databases, a breach here allows an attacker to manipulate the model’s data flow or exfiltrate information via prompt injection. Administrative dashboards, on the other hand, are the control centers used to update models and monitor performance; compromising one gives an attacker the power to alter the model’s behavior entirely or shut down services. We frequently see these become reachable from the public internet due to “gradual exposure,” where a developer might disable authentication on an API gateway to speed up a 48-hour testing phase, only to forget to re-enable it. To secure these, you must first transition from implicit trust to explicit verification, ensuring that every gateway and firewall rule is audited against a central security policy. Second, you must replace all static tokens with dynamic authentication, and finally, implement continuous monitoring to catch misconfigured cloud rules before they are indexed by malicious scanners.
Development teams often assume internal networks are inherently secure, frequently relying on static, hardcoded tokens for speed. How does this “internal-only” mindset fail when VPNs or cloud gateways are misconfigured, and what specific steps ensure temporary test endpoints do not become permanent, unmonitored vulnerabilities?
The “internal-only” mindset is perhaps the most dangerous relic of legacy security because it ignores the fact that internal networks are now highly fluid and reachable through misconfigured VPNs or cloud peering. When a team hardcodes a static token into a configuration file for a “temporary” test, they are essentially creating a permanent back door that never expires and is rarely rotated. If a cloud gateway is accidentally set to “public” during a routine update, that internal endpoint is suddenly screaming its presence to the entire world. To prevent these test endpoints from becoming “zombie vulnerabilities,” organizations need to automate the lifecycle of an endpoint; if an API hasn’t been audited or used within a specific window, it should be auto-isolated. Furthermore, we must move away from static credentials entirely, using short-lived tokens that expire in minutes rather than months, ensuring that even if a secret is leaked, its utility to an attacker is almost zero.
A compromised LLM endpoint can act as a force multiplier for attackers who exploit tool-calling permissions or prompt-driven data exfiltration. How does a single breach allow for lateral movement across connected cloud services, and what specific metrics or indicators should security teams monitor to detect this automated exploitation?
Because LLMs are designed to be “connectors” that bridge multiple systems, they are often granted implicit trust by the databases and cloud services they interact with. When a single endpoint is compromised, the attacker inherits the identity of the model, allowing them to use tool-calling permissions to browse internal file systems or execute code in connected cloud environments. This is a “force multiplier” because the attacker can use the LLM to automate the extraction of sensitive data, such as asking the model to summarize 1,000 sensitive documents it has access to in seconds. Security teams need to monitor “identity-to-data” velocity—if a service account suddenly starts accessing a volume of data three times its normal baseline, that’s a red flag. You should also watch for unusual tool-invocation patterns, such as an LLM endpoint calling administrative functions or external APIs that are outside its documented operational scope.
Service accounts and API keys often accumulate excessive permissions that are rarely revisited, leading to dangerous secrets sprawl. How do these non-human identities complicate the security perimeter, and what is the practical impact of using long-lived, static credentials instead of automated rotation in high-velocity AI workloads?
Non-human identities (NHIs) are now the dominant “users” in AI infrastructure, and they complicate the perimeter because they operate 24/7 without human intervention, often with broad permissions granted “just to make sure it works.” This leads to secrets sprawl, where API keys are scattered across configuration files, CI/CD pipelines, and developer environments, making them nearly impossible to track manually. Using long-lived, static credentials in this environment is like leaving a master key under the doormat of a house that is constantly being remodeled; eventually, someone unauthorized will find it. In high-velocity workloads, the practical impact of failing to rotate these keys is that a single leak provides a permanent, silent foothold for an attacker to move laterally. Automated rotation is the only way to shorten this window of opportunity, ensuring that even if a credential is captured, it becomes a useless string of characters before the attacker can fully map the network.
Shifting toward a zero-trust model requires enforcing least-privilege access and adopting Just-in-Time permissions. How can organizations practically implement these controls for automated systems that require continuous uptime, and what role does session recording play in auditing these non-human interactions? Please share an anecdote regarding implementation challenges.
Implementing Just-in-Time (JIT) permissions for automated systems means that instead of an LLM having “standing access” to a database, it is granted a credential that only exists for the duration of a specific task. For continuous uptime, this requires a robust orchestration layer that can issue and revoke these “ephemeral” identities without interrupting the service flow. Session recording is vital here because it provides a forensic trail of what the “machine” actually did; it turns a black box of API calls into a readable history for auditors. I remember a case with a major client where we tried to move their LLM to a least-privilege model, and the system immediately crashed because the model had been secretly relying on an “owner” level permission to access a legacy logging bucket. It took us 72 hours of debugging to realize how “permission creep” had become a structural dependency, proving that you can’t secure what you don’t fully map out first.
What is your forecast for LLM infrastructure security?
I believe we are moving toward an era where “Identity is the New Perimeter,” and my forecast is that within the next two years, the industry will pivot away from traditional network firewalls toward automated Identity Threat Detection and Response (ITDR) specifically for non-human entities. As LLMs become more autonomous, the risk will no longer be about “hacking the model” via a clever prompt, but about the “silent takeover” of the service accounts that allow the model to act on the world. We will see a mandatory shift toward ephemeral, short-lived credentials as the standard for all AI workloads, effectively killing off the era of the static API key. Organizations that fail to automate their secrets management and continue to rely on manual audits will find themselves unable to keep up with the speed of AI-driven attacks, making zero-trust architecture a requirement for survival rather than a luxury.
