In Episode Seventy-Five, titled “Deserialization and File Inclusion Concepts,” we’re focusing on how unsafe data handling can lead to severe control, even when the application appears to be doing something ordinary like reading a parameter or parsing a cookie. These issues are dangerous because they sit at a boundary where the application translates data into behavior: deserialization turns a blob of data into objects the runtime may treat as active, and file inclusion turns a user-supplied value into a decision about what code or templates to load. In both cases, the attacker’s goal is not to break encryption or guess passwords; it’s to influence what the application interprets as trusted structure. The exam tends to test your ability to recognize clue patterns, reason about impact, and choose safe confirmation steps rather than jumping into destructive payloads. The core idea is simple: when untrusted data controls what gets executed or loaded, risk rises fast. This episode gives you a clean mental model so you can classify scenarios and recommend controls confidently.
Deserialization can be described simply as turning data into objects that can execute behavior, because objects in many runtimes are more than just data containers. Applications serialize objects to store them or transmit them, then deserialize them later to reconstruct the original objects in memory. When that deserialization process accepts attacker-controlled data, the attacker may be able to influence what objects are created and how they behave during reconstruction. The key is that deserialization can invoke object constructors, callbacks, or other automatic behaviors as part of rebuilding the object graph. That means the act of parsing can do work, which is fundamentally different from parsing simple text or JSON values that are treated as inert. On the exam, you don’t need to memorize specific gadget chains, but you do need to recognize that deserialization is dangerous when the input is untrusted and the runtime can be coaxed into executing unintended actions. The mental model is that a data blob can become a set of runtime instructions if the deserializer is too permissive.
It is risky because attacker-controlled data can trigger unintended actions at the moment the application turns the data back into active structures. Even if the application “only” intended to restore a user profile or session state, a maliciously crafted blob can cause object creation sequences that lead to dangerous side effects. In many environments, this can lead to code execution, but even short of that, it can cause authentication bypass, manipulation of application logic, or denial-of-service through resource-heavy object graphs. The risk increases when the application trusts serialized state for security decisions, such as using a serialized object to represent user role, permissions, or session attributes. It also increases when debugging or error handling reveals internal class names and stack traces, because those clues can guide an attacker toward exploitable paths. The bigger lesson is that complex object reconstruction is a security-sensitive operation and should not be driven by untrusted input. When you treat deserialization as “parsing with side effects,” the threat becomes intuitive.
File inclusion can be described simply as the application loading files based on user input, which becomes risky when user input influences what file is loaded without strict control. Many applications load templates, language packs, content fragments, or configuration files dynamically to support modular design. If the filename or path is directly influenced by a request parameter, cookie value, or header, an attacker may be able to request unintended files. This can lead to reading sensitive local files, loading unintended templates, or in some cases causing the application to execute code from unexpected locations. The vulnerability is not that the application reads files; the vulnerability is that the application allows untrusted input to choose which file is read or included. Inclusion problems often show up in features like “page=template,” “lang=en,” “view=report,” or similar controls that map parameters to files. The mental model is that user input is being treated as a file selector, which should always be tightly constrained.
Local versus remote inclusion is a helpful conceptual distinction because it clarifies what the attacker is trying to include. Local inclusion involves reading local files on the server, such as configuration files, credentials, system files, or application source files, often enabled by path traversal patterns that escape the intended directory. Remote inclusion involves fetching external content and including it, which can become a direct route to executing attacker-controlled code if the application evaluates what it fetches or if the inclusion mechanism treats remote content as executable templates. Remote inclusion is less common in modern frameworks with safer defaults, but the concept still matters for exam reasoning, because it describes an application loading external resources based on input without strict destination controls. Both local and remote inclusion share the same core failure: the input is controlling what gets loaded, and the application is not enforcing strict rules about what is allowed. The difference is the source of the loaded content, which influences impact and remediation. If you can name whether the path points inward to local files or outward to external resources, you can reason about likely outcomes more accurately.
Clue patterns matter because deserialization and inclusion rarely announce themselves with a friendly label; they show up as odd errors and unexpected behaviors. Error traces are a strong clue, especially when an input blob triggers stack traces that reference object reconstruction, parsing, or specific classes, suggesting a deserialization path. Unexpected file contents are a clue for inclusion, such as when a request returns fragments that resemble local files, templates, or configuration values rather than normal page output. Path traversal signs are another inclusion clue, such as errors referencing “file not found” in unexpected directories or behavior changes when you adjust directory-like elements in a parameter. For both issues, inconsistent behavior tied to structured input is important, such as a base64-like value that changes error behavior dramatically when modified slightly. Another clue is that the application appears to treat a parameter as a filename or a serialized state handle rather than a simple value. The professional approach is to treat these clues as hypotheses and then validate with minimal, controlled checks rather than assuming the worst immediately.
Impact can be severe, and the exam often expects you to reason across information disclosure, authentication bypass, and remote code execution potential without overstating what you can prove. File inclusion can lead to information disclosure if an attacker can read sensitive files, and it can lead to authentication bypass if the attacker can load configuration or logic that influences access decisions. In some cases, inclusion can contribute to code execution if the attacker can include a file that is executable or can influence the application to execute injected content. Deserialization can lead to authentication bypass when serialized state is trusted for identity decisions, and it can lead to code execution when gadget chains exist in the runtime and the input is attacker-controlled. Both can also cause disruption, such as crashing the application or causing heavy resource consumption, because parsing and file operations can be expensive. The safe reporting mindset is to describe what was observed, describe plausible impact based on the class of issue, and avoid claiming full compromise unless you have evidence. The interpreter and privilege context matters, so you tie impact to what the application is actually doing with the input.
Now consider a scenario where a cookie-like blob triggers server errors, which is a common way deserialization risk shows up. Imagine the application sets a structured value in a cookie or parameter that looks encoded, and when you slightly modify it, the server responds with errors that reference object parsing or internal class handling. The clue is that the application appears to treat the blob as structured state rather than as a simple identifier, and that small changes cause parsing failures rather than clean rejection. That pattern suggests deserialization because it looks like the server is attempting to reconstruct an object from the blob and failing during that reconstruction. The safest next step is to confirm that the blob is indeed driving server-side reconstruction, such as by observing consistent error behavior when the structure is altered, without attempting heavy payloads or destructive tests. You focus on demonstrating that attacker-controlled input is being deserialized and that the deserialization path is reachable, because that is enough to justify remediation work. In many engagements, proving unsafe deserialization is present is more valuable than attempting deeper exploitation, especially on production systems.
A second scenario involves a page parameter loading templates, which is a classic inclusion risk pattern. Imagine a request contains a parameter that appears to select a view, and when you vary the parameter, different templates load, sometimes with errors indicating missing files or unexpected path lookups. The clue is that the parameter is acting like a file selector rather than a purely logical option, which means user input is influencing what the application attempts to load. The safest validation approach is to confirm the mapping and boundaries: what values are accepted, whether the application restricts values to a fixed set, and whether the application prevents directory traversal or unintended path resolution. You avoid trying to pull sensitive files as a proof stunt, and instead you aim to show that the application is attempting to load files based on untrusted input and that the restriction is insufficient. If you can demonstrate that the application’s path resolution escapes intended boundaries or that it attempts to resolve arbitrary file paths, you have credible evidence of inclusion risk. This is a boundary problem, and the safest proof is boundary evidence, not data extraction.
A key pitfall is assuming every file read equals an inclusion vulnerability without proof, because applications legitimately read files all the time. Many frameworks load templates, static assets, configuration, and translations safely because the file selection is not controlled by the user or is strictly mapped. If you treat “it loads a template” as “it has file inclusion,” you will misreport and lose credibility when developers show that an allowlist mapping exists. Another pitfall is confusing normal error handling with vulnerability evidence, such as interpreting a “file not found” message as proof of traversal without confirming that the input actually influenced the path outside intended values. For deserialization, a pitfall is assuming that an encoded blob must be serialized object data when it could be a signed token or a simple encrypted value with safe handling. The professional approach is to confirm that input controls selection or reconstruction in a way that is not tightly constrained. Evidence beats assumption, especially with these high-impact categories.
Remediation concepts focus on reducing power at the boundary by using safer formats and strict allowlists. For deserialization, a key concept is using safe serialization formats that are treated as data, such as strict JSON structures with explicit schema validation, rather than object graphs that can trigger automatic behaviors. Another concept is to avoid trusting client-supplied serialized state for security decisions, and to ensure that any state stored client-side is integrity-protected and minimal. For file inclusion, strict allowlists are central: the application should map user-facing options to known-safe templates or resources rather than allowing arbitrary file paths. Input validation helps, but allowlists and fixed mappings are stronger than trying to strip dangerous characters because path handling edge cases are subtle. Defense-in-depth controls like running services with least privilege and restricting filesystem access also reduce impact if a boundary is bypassed. The exam-friendly summary is that both issues require strict control over what data can become and what resources can be loaded.
Reporting language should show the input, the observed behavior, and the plausible impact carefully, because these topics can alarm stakeholders if phrased irresponsibly. You identify the input source, such as a cookie, parameter, or header, and you describe how changes to that input affected server behavior. You describe the behavior evidence, such as parsing errors consistent with unsafe deserialization or file resolution behavior consistent with insufficient template selection controls. You describe impact in a measured way, such as the potential for unauthorized object reconstruction behavior, information disclosure, or broader compromise if the path can be chained, while staying within what you can reasonably infer. You recommend controls that match the issue, such as replacing unsafe serialization mechanisms, enforcing strict schema validation, implementing allowlist-based template selection, and tightening filesystem permissions. You also note constraints and safe validation decisions, emphasizing that you avoided destructive tests and sensitive data extraction. This reporting style preserves trust and drives practical remediation.
To keep the concepts sticky, use this memory anchor: objects execute, files include, both need strict control. Objects execute reminds you that deserialization turns data into active runtime structures, so untrusted input can trigger behavior. Files include reminds you that inclusion turns input into a resource-loading decision, so weak control can expose unintended files or code paths. Both need strict control reminds you that the right defense is not clever filtering, but strongly limiting what the application will accept and load. This anchor also helps you avoid tunnel vision, because it reminds you that both categories are boundary problems where user input influences something powerful. When you repeat it, you naturally gravitate toward safe validation and allowlist-based remediation. It is a simple phrase that aligns classification, testing, and fixes.
To conclude Episode Seventy-Five, titled “Deserialization and File Inclusion Concepts,” remember that these issues are dangerous because they convert untrusted input into behavior: either by reconstructing objects that can execute during parsing or by choosing files that the application will load. Clues like structured blob errors and template-loading parameters guide your hypothesis, but safe confirmation relies on minimal, controlled evidence rather than destructive exploration. For safe controls, choose one for each risk: for deserialization, prefer safe data-only formats with strict schema validation instead of trusting object deserialization from untrusted sources, and for file inclusion, enforce strict allowlist-based mapping from user options to known templates rather than accepting arbitrary paths. If you keep those two controls in mind, you’ll have a practical and exam-ready way to reason about both categories without overcomplicating them.