Static Analysis
An analysis technique that determines code correctness without executing the program, by parsing source code syntax trees, symbol references and structural patterns. It is fast, reproducible and deterministic, and requires no sandboxed execution.
Timeline (last 90 days)
Contracts can serve as input to static analysis tools; even if runtime checks are disabled, the tool can still infer potential null pointer dereferences or array out-of-bounds based on the condition declarations.
The benefit of contracts for static analysis is not free; the premise is that the analysis tool can be certain that the contract will actually be checked at runtime or be treated as inviolable, otherwise false positives or false negatives will occur.
Runtime verification can catch problems that static analysis cannot find, such as database transaction deadlocks, duplicate consumption in message queues, state inconsistency after external API timeouts, and race conditions under concurrency.
By default, bundlers only include in the output modules that can be statically identified as being referenced; dynamically concatenated module names, conditional loading, and packages that plugins import on demand at runtime may all be misjudged as unused and removed
The advantages of static analysis are that it is fast, reproducible, and deterministic, and does not rely on a sandbox environment; the cost is that it cannot discover problems that only surface at runtime, such as crashes from type errors, asynchronous timing issues, or differences in API behavior under specific versions.
Because the evaluation does not execute the code, a build plan that passes static analysis may still fail at runtime.
Tools like Deslop typically need to trade off between rule strictness and false positive rate: the more aggressive the rules, the more redundancy they can clean up, but the more likely they are to flag safety code humans deliberately wrote as a problem.
Static analysis tools, without running the code, discover potential issues by parsing the abstract syntax tree (AST) and building data flows; their advantage is speed and automation, but the cost is a lack of runtime context, which easily produces false positives.
Static analysis cannot detect TOCTOU attacks on agent skills because the content being analyzed is not the content being executed, and the substitution can occur between the scan and the next invocation.
Static analysis parses the instruction sequences of executable files directly without executing the program and covers all code paths, but the difficulty increases sharply when faced with packing or virtualization-based protection.
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All Facts (20)
GitNexus在构建知识图谱时执行多层次静态分析：语法层面通过AST解析，语义层面通过符号解析追踪跨文件引用，模块层面分析import/require/include依赖声明
90%UnverifiedContracts can serve as input to static analysis tools; even if runtime checks are disabled, the tool can still infer potential null pointer dereferences or array out-of-bounds based on the condition declarations.
50%UnverifiedThe benefit of contracts for static analysis is not free; the premise is that the analysis tool can be certain that the contract will actually be checked at runtime or be treated as inviolable, otherwise false positives or false negatives will occur.
50%UnverifiedRuntime verification can catch problems that static analysis cannot find, such as database transaction deadlocks, duplicate consumption in message queues, state inconsistency after external API timeouts, and race conditions under concurrency.
50%UnverifiedBy default, bundlers only include in the output modules that can be statically identified as being referenced; dynamically concatenated module names, conditional loading, and packages that plugins import on demand at runtime may all be misjudged as unused and removed
50%UnverifiedBecause the evaluation does not execute the code, a build plan that passes static analysis may still fail at runtime.
50%UnverifiedThe advantages of static analysis are that it is fast, reproducible, and deterministic, and does not rely on a sandbox environment; the cost is that it cannot discover problems that only surface at runtime, such as crashes from type errors, asynchronous timing issues, or differences in API behavior under specific versions.
50%UnverifiedStatic analysis tools, without running the code, discover potential issues by parsing the abstract syntax tree (AST) and building data flows; their advantage is speed and automation, but the cost is a lack of runtime context, which easily produces false positives.
50%UnverifiedTools like Deslop typically need to trade off between rule strictness and false positive rate: the more aggressive the rules, the more redundancy they can clean up, but the more likely they are to flag safety code humans deliberately wrote as a problem.
50%UnverifiedStatic analysis cannot detect TOCTOU attacks on agent skills because the content being analyzed is not the content being executed, and the substitution can occur between the scan and the next invocation.
50%UnverifiedStatic analysis parses the instruction sequences of executable files directly without executing the program and covers all code paths, but the difficulty increases sharply when faced with packing or virtualization-based protection.
50%UnverifiedStatic analysis cannot fully cover emergent behaviors of AI agents that depend on runtime context, and therefore SafeAI is a first line of defense rather than a complete security guarantee.
50%UnverifiedSafeAI employs a static analysis approach, inferring an agent's potential behavior by examining metadata such as configuration files, system prompts, tool definitions, and permission declarations, without actually running the agent.
50%UnverifiedStatic analysis refers to examining a program without actually running it, in contrast to dynamic analysis, which requires actually executing code to observe behavior.
50%UnverifiedStatic analysis tools such as SonarQube and Semgrep, as well as formal verification, have long served an automated quality-gating role, but they target human-written code and their rule sets are maintained manually.
50%UnverifiedTechnical directions for detecting intent conflicts include static analysis with data-flow tracking, semantic comparison of task descriptions, and LLM-based diff review.
50%UnverifiedStatic analysis has a low false-positive rate but limited coverage, while LLM review has broad coverage but is unstable and expensive.
50%UnverifiedMethods such as fuzzing, static analysis, and LLM-as-verifier struggle to cover all possible edge cases.
50%UnverifiedRice's theorem states that for Turing-complete languages, any non-trivial semantic property is undecidable in the general case, which constitutes the core theoretical limitation of static analysis tools.
50%UnverifiedStatic analysis tools must make trade-offs between precision and recall: being too strict produces a large number of false positives, while being too lenient misses real bugs.
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